{ "brief_id": "SPR-2026-F859", "generated_at": "2026-10-18", "domains": [ "Botany and Plant Ecology Studies", "Single-cell and spatial transcriptomics" ], "original_hypothesis": "Single-cell/spatial transcriptomics can be repurposed as a high-resolution tool to study plant–microbe interactions and microbial niche colonization in planta, enabling the discovery of plant cell-type-specific responses to pathogens and the identification of microbial effector genes expressed during infection.", "grounding": { "novelty_assessment": { "score": 0.45, "closest_existing_work": [ { "paper_id": "ccf3821db8f31006aa98c8a7fe83c5dc7614d6b7", "title": "Unraveling plant-microbe symbioses using single-cell and spatial transcriptomics.", "doi": "10.1016/j.tplants.2024.06.008", "year": 2024, "similarity": "very_high", "key_difference": "This review already articulates the exact methodological transfer (scRNA-seq/ST applied to plant-microbe interactions) but focuses on symbioses/endosymbioses rather than pathogens and effector discovery; the SPORE hypothesis extends to pathosystems and microbial effector gene mapping in situ.", "authors": [ "K. Serrano", "Francesca Tedeschi", "S. U. Andersen", "Henrik V. Scheller" ] }, { "paper_id": "0034103ca4deb5f7d62ac96b873bbd8d4e6a6666", "title": "Time-resolved single-cell and spatial gene regulatory atlas of plants under pathogen attack", "doi": "10.1101/2023.04.10.536170", "year": 2023, "similarity": "very_high", "key_difference": "Already demonstrates time-resolved scRNA-seq + spatial transcriptomics of Arabidopsis infected by Pseudomonas syringae, mapping plant immune responses to pathogen distribution — this is essentially a proof-of-concept of the hypothesis in a bacterial pathosystem.", "authors": [ "Tatsuya Nobori", "Alexander Monell", "Travis A. Lee", "Jing-Tian Zhou", "J. Nery", "J. Ecker" ] }, { "paper_id": "db35af580d6d1df4aac1d9a0045f2d4508c05b06", "title": "Cell type-specific responses to fungal infection in plants revealed by single-cell transcriptomics", "doi": "10.1101/2023.04.03.535386", "year": 2023, "similarity": "high", "key_difference": "Provides a single-cell atlas of Arabidopsis leaves challenged by Colletotrichum higginsianum with cell-type-specific responses and interaction assignment — again a concrete realization of part of the hypothesis, though without spatial transcriptomics or microbial effector mapping.", "authors": [ "Bo-Zeng Tang", "Li Feng", "M. Hulin", "Ping-Tao Ding", "Wenbo Ma" ] }, { "paper_id": "f7cd263cc2e1a8b49a947b2de050565c7733f051", "title": "Mapping plant cell-type-specific responses to environmental stresses.", "doi": "10.1016/j.tplants.2026.03.003", "year": 2026, "similarity": "high", "key_difference": "Review explicitly covering scRNA-seq/ST for mapping cell-type-specific responses to pathogen attack; overlaps strongly with the plant-response half of the hypothesis but does not emphasize microbial effector discovery or dual-species capture.", "authors": [ "M. Ali", "Zheng-Jia Wang", "Qiu-Yue Guo", "Yixiang Wang", "Yilang Cai", "Juan Du", "E. Pi", "Ping-Tao Ding", "Jinbo Shen" ] } ], "verdict": "incremental" }, "evidence_base": [ { "paper_id": "0034103ca4deb5f7d62ac96b873bbd8d4e6a6666", "title": "Time-resolved single-cell and spatial gene regulatory atlas of plants under pathogen attack", "doi": "10.1101/2023.04.10.536170", "year": 2023, "citation_count": 35, "support_type": "direct", "relevance": "Directly demonstrates scRNA-seq + spatial transcriptomics on pathogen-infected Arabidopsis, resolving plant cell responses linked to pathogen distribution — the core methodological claim of the hypothesis.", "key_finding": "Time-resolved single-cell transcriptome and epigenome profiling of Arabidopsis infected by virulent/avirulent Pseudomonas syringae, with spatial transcriptomics revealing heterogeneity of immune responses linked to pathogen distribution.", "authors": [ "Tatsuya Nobori", "Alexander Monell", "Travis A. Lee", "Jing-Tian Zhou", "J. Nery", "J. Ecker" ] }, { "paper_id": "db35af580d6d1df4aac1d9a0045f2d4508c05b06", "title": "Cell type-specific responses to fungal infection in plants revealed by single-cell transcriptomics", "doi": "10.1101/2023.04.03.535386", "year": 2023, "citation_count": 101, "support_type": "direct", "relevance": "Shows scRNA-seq of infected plant tissue yields cell-type-specific responses and assigns cells interacting with invasive hyphae — supports the 'which cell types are colonized and how they respond' claim.", "key_finding": "Single-cell atlas of Arabidopsis leaves challenged by Colletotrichum higginsianum reveals cell-type-specific gene expression, enrichment of intracellular immune receptors in vasculature, and trajectory-based assignment of interacting cells.", "authors": [ "Bo-Zeng Tang", "Li Feng", "M. Hulin", "Ping-Tao Ding", "Wenbo Ma" ] }, { "paper_id": "ccf3821db8f31006aa98c8a7fe83c5dc7614d6b7", "title": "Unraveling plant-microbe symbioses using single-cell and spatial transcriptomics.", "doi": "10.1016/j.tplants.2024.06.008", "year": 2024, "citation_count": 39, "support_type": "direct", "relevance": "Explicitly argues that scRNA-seq/ST enable probing plant-microbe interactions at unprecedented resolution, validating the conceptual transfer (though framed for symbioses).", "key_finding": "Reviews advantages and remaining limitations of applying single-cell and spatial transcriptomics to plant-microbe endosymbioses.", "authors": [ "K. Serrano", "Francesca Tedeschi", "S. U. Andersen", "Henrik V. Scheller" ] }, { "paper_id": "f7cd263cc2e1a8b49a947b2de050565c7733f051", "title": "Mapping plant cell-type-specific responses to environmental stresses.", "doi": "10.1016/j.tplants.2026.03.003", "year": 2026, "citation_count": 89, "support_type": "direct", "relevance": "States that scRNA-seq/ST enable high-resolution mapping of transcriptional programs across cell types in response to pathogen attack, supporting the plant-response half of the hypothesis.", "key_finding": "Review highlighting scRNA-seq/snRNA-seq and ST for mapping cell-type-specific transcriptional programs under biotic and abiotic stress.", "authors": [ "M. Ali", "Zheng-Jia Wang", "Qiu-Yue Guo", "Yixiang Wang", "Yilang Cai", "Juan Du", "E. Pi", "Ping-Tao Ding", "Jinbo Shen" ] }, { "paper_id": "fdde28d13fa64e257a0ecaf90807eeca55ae7e14", "title": "Cell type-specific transcriptomics reveals that root hairs and endodermal barriers play important roles in beneficial plant-rhizobacterium-interactions", "doi": "10.1016/j.molp.2023.06.001", "year": 2023, "citation_count": 40, "support_type": "indirect", "relevance": "Demonstrates cell-type-specific transcriptional profiling of root cell types during plant-microbe (beneficial bacterium) interaction, supporting feasibility of resolving cell-type-specific responses to microbes.", "key_finding": "Profiling five Arabidopsis root cell types uncovered masked mechanisms underlying beneficial plant-microbe associations and the role of endodermal barrier integrity.", "authors": [ "E. Verbon", "Louisa M. Liberman", "Jia-Yu Zhou", "Jie Yin", "C. Pieterse", "P. Benfey", "Ioannis A. Stringlis", "R. de Jonge" ] }, { "paper_id": "d5e1b22d88e8a1a237761ba598975adc6a38fa02", "title": "Mechanisms of rhizosphere plant-microbe interactions: molecular insights into microbial colonization", "doi": "10.3389/fpls.2024.1491495", "year": 2024, "citation_count": 110, "support_type": "indirect", "relevance": "Establishes the biological question (microbial colonization mechanisms, chemotaxis, root attachment, immune evasion) that the hypothesis proposes to resolve at cellular resolution; supports the 'gap' framing.", "key_finding": "Reviews molecular mechanisms of rhizosphere colonization including chemotaxis, root attachment, immune evasion, and biofilm formation.", "authors": [ "Lu-Na Yang", "Xin Qian", "Zeyu Zhao", "Yao-Yao Wang", "G. Ding", "Xiao-Ke Xing" ] }, { "paper_id": "f53415cd1954b9741766c4aa3b2eade53417b3e4", "title": "High-Resolution Transcript Profiling of the Atypical Biotrophic Interaction between Theobroma cacao and the Fungal Pathogen Moniliophthora perniciosa[C][W][OPEN]", "doi": "10.1105/tpc.114.130807", "year": 2014, "citation_count": 101, "support_type": "indirect", "relevance": "Bulk dual-species transcript profiling identified putative fungal virulence effectors during infection — the kind of effector discovery the hypothesis aims to push to single-cell/spatial resolution.", "key_finding": "Characterized fungal nutritional and infection strategies during witches' broom disease and identified putative virulence effectors via high-resolution bulk transcript profiling.", "authors": [ "P. Teixeira", "Daniela Paula de Toledo Thomazella", "O. Reis", "P. F. V. Do Prado", "M. C. S. Do Rio", "G. L. Fiorin", "J. José", "Gustavo G. L. Costa", "V. A. Negri", "J. M. Mondego", "P. Mieczkowski", "G. G. Pereira" ] }, { "paper_id": "ef584f6dad945c0f01cd3e8286e42cb65f1e7b31", "title": "A robust method of nuclei isolation for single-cell RNA sequencing of solid tissues from the plant genus Populus", "doi": "10.1371/journal.pone.0251149", "year": 2021, "citation_count": 39, "support_type": "indirect", "relevance": "Provides a nuclei-isolation method (snRNA-seq) for plant solid tissues, supporting the technical feasibility of the nuclei-isolation route proposed in the mechanism.", "key_finding": "Presents a simple nuclei-isolation approach generating high-quality mRNA for 10x Genomics Chromium scRNA-seq from plant tissue.", "authors": [ "D. Conde", "P. M. Triozzi", "K. Balmant", "A. Doty", "M. Miranda", "A. Boullosa", "H. W. Schmidt", "W. Pereira", "C. Dervinis", "M. Kirst" ] }, { "paper_id": "d552a44a3bc33d987823f52df4b71793bd757823", "title": "An Efficient and Universal Protoplast Isolation Protocol Suitable for Transient Gene Expression Analysis and Single-Cell RNA Sequencing", "doi": "10.3390/ijms23073419", "year": 2022, "citation_count": 39, "support_type": "indirect", "relevance": "Provides a protoplast isolation protocol for scRNA-seq, supporting the protoplasting route in the proposed mechanism.", "key_finding": "Establishes a simple, efficient protoplast preparation protocol enabling scRNA-seq in a non-model plant species.", "authors": [ "Juanjuan Wang", "Yang Wang", "Tian-Feng Lu", "Xia Yang", "Jing Liu", "Yang Dong", "Yin-Zheng Wang" ] }, { "paper_id": "9b4195dd8125bd09ade3622a5185e379bc316f55", "title": "Integrated single-cell and spatial transcriptomics uncover distinct cellular subtypes involved in neural invasion in pancreatic cancer.", "doi": "10.1016/j.ccell.2025.06.020", "year": 2025, "citation_count": 119, "support_type": "analogous", "relevance": "The cancer analog invoked in the hypothesis: integrated sc/snRNA-seq + ST mapping cellular composition and spatial organization at an invasion front. Supports the methodological analogy but not plant biology.", "key_finding": "sc/snRNA-seq and spatial transcriptomics across 62 PDAC samples map cellular composition, lineage dynamics, and spatial organization across neural invasion statuses.", "authors": [ "Min-Min Chen", "Qin-Hang Gao", "Hui-Heng Ning", "Kang Chen", "Yang Gao", "Min Yu", "Chao Liu", "Wei Zhou", "Jie-Ying Pan", "Lu-Sheng Wei", "Wen-Xian Dou", "Ding-Wen Zhang", "Lin-Na Zhu", "Qing-Ling Zhang", "Ru-Fu Chen", "Zemin Zhang" ] }, { "paper_id": "7cbc346bf4ff64a5b254f69f4e9d4bf475458cbd", "title": "Niche-DE: niche-differential gene expression analysis in spatial transcriptomics data identifies context-dependent cell-cell interactions", "doi": "10.1186/s13059-023-03159-6", "year": 2024, "citation_count": 57, "support_type": "analogous", "relevance": "Provides a computational framework for identifying cell-type-specific niche-associated genes in spatial data — relevant to the 'computational integration' step of the proposed mechanism, though not plant-specific.", "key_finding": "Proposes niche-DE analysis identifying cell-type-specific niche-associated genes and underlying ligand-receptor signaling in spatial transcriptomics data.", "authors": [ "Kaishu Mason", "Anuja Sathe", "Paul R. Hess", "Jiazhen Rong", "Chi-Yun Wu", "Emma F. Furth", "K. Suszták", "Jonathan Levinsohn", "Hanlee P. Ji", "Nancy R. Zhang" ] }, { "paper_id": "cca97ab86fc094eff85542ebce0d2e26eb72a001", "title": "Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq", "doi": "10.1126/science.aad0501", "year": 2016, "citation_count": 4549, "support_type": "analogous", "relevance": "Foundational demonstration that scRNA-seq resolves heterogeneous cell states/subpopulations in a complex tissue — the conceptual template for resolving plant cell-type heterogeneity.", "key_finding": "scRNA-seq of melanoma revealed heterogeneous genetic programs and distinct cell states linked to drug resistance.", "authors": [ "I. Tirosh", "B. Izar", "S. Prakadan", "M. Wadsworth", "D. Treacy", "John J. Trombetta", "Asaf Rotem", "Christopher Rodman", "Christine G. Lian", "G. Murphy", "M. Fallahi-Sichani", "K. Dutton-Regester", "Jia-Ren Lin", "O. Cohen", "P. Shah", "Diana Y. Lu", "Alex S. Genshaft", "T. Hughes", "Carly G. K. Ziegler", "S. Kazer", "A. Gaillard", "Kellie E. Kolb", "A. Villani", "C. Johannessen", "Aleksandr Andreev", "E. V. Van Allen", "M. Bertagnolli", "P. Sorger", "R. Sullivan", "K. Flaherty", "Dennie T. Frederick", "Judit Jané-Valbuena", "Charles H. Yoon", "O. Rozenblatt-Rosen", "A. Shalek", "A. Regev", "L. Garraway" ] } ], "counter_evidence": [ { "paper_id": "0034103ca4deb5f7d62ac96b873bbd8d4e6a6666", "title": "Time-resolved single-cell and spatial gene regulatory atlas of plants under pathogen attack", "doi": "10.1101/2023.04.10.536170", "finding": "This preprint already implements scRNA-seq + spatial transcriptomics on pathogen-infected plants, meaning the hypothesis is not novel as a concept — it is already being executed. It does not, however, appear to demonstrate large-scale microbial effector gene mapping, so the effector-discovery component remains less covered.", "severity": "serious", "authors": [ "Tatsuya Nobori", "Alexander Monell", "Travis A. Lee", "Jing-Tian Zhou", "J. Nery", "J. Ecker" ] }, { "paper_id": "ccf3821db8f31006aa98c8a7fe83c5dc7614d6b7", "title": "Unraveling plant-microbe symbioses using single-cell and spatial transcriptomics.", "doi": "10.1016/j.tplants.2024.06.008", "finding": "Explicitly notes remaining limitations of applying these approaches to plant-microbe systems, indicating technical barriers (not a refutation of the hypothesis but a constraint on feasibility).", "severity": "addressable", "authors": [ "K. Serrano", "Francesca Tedeschi", "S. U. Andersen", "Henrik V. Scheller" ] }, { "paper_id": "76ebf7049a2e8fc44ef228cb1547e1b22fe83cfe", "title": "ASV vs OTUs clustering: Effects on alpha, beta, and gamma diversities in microbiome metabarcoding studies", "doi": "10.1371/journal.pone.0309065", "finding": "Not directly contradictory, but highlights that microbial community profiling has methodological biases; tangential to the dual-species transcript capture claim and does not address scRNA-seq capture of microbial transcripts.", "severity": "minor", "authors": [ "Andrea Fasolo", "Saptarathi Deb", "P. Stevanato", "G. Concheri", "A. Squartini" ] } ], "gap_manifest_update": { "closed_gaps": [ "Lack of published single-cell or spatial transcriptomics datasets for plant–microbe interactions — partially closed: at least two datasets (Arabidopsis–Pseudomonas, Arabidopsis–Colletotrichum) now exist as preprints, contradicting the 'no datasets' framing.", "Feasibility of applying scRNA-seq/ST to plant pathosystems — closed at proof-of-concept level by the Arabidopsis–Pseudomonas and Arabidopsis–Colletotrichum studies." ], "new_gaps": [ "Microbial effector gene discovery at single-cell/spatial resolution in planta remains largely undemonstrated; existing plant pathosystem atlases focus on plant responses rather than microbial in situ gene expression.", "Dual-species (host+microbe) transcript binning and assignment of microbial reads to plant cell clusters in plant scRNA-seq is not yet validated at scale.", "Spatial transcriptomics on infected plant tissues (cell wall, autofluorescence, permeabilization) remains technically constrained; no established protocols in the provided literature." ], "data_available": [ "Arabidopsis thaliana–Pseudomonas syringae time-resolved single-cell and spatial atlas (Nobori et al., 2023 preprint)", "Arabidopsis thaliana–Colletotrichum higginsianum single-cell atlas (Tang et al., 2023 preprint)", "Arabidopsis root cell-type-specific transcriptomes during Pseudomonas simiae WCS417 colonization (Verbon et al., 2023)", "Bulk dual-species transcriptome of Theobroma cacao–Moniliophthora perniciosa with putative effector identification (Teixeira et al., 2014)" ] }, "search_queries": [ { "query": "single-cell RNA-seq plant-microbe interaction", "type": "novelty", "rationale": "Directly tests whether scRNA-seq has been applied to plant-microbe pathosystems, the core novelty claim." }, { "query": "spatial transcriptomics plant pathogen infection", "type": "novelty", "rationale": "Checks for existing spatial transcriptomics studies on infected plant tissues, addressing the ST component of the hypothesis." }, { "query": "single-cell microbial niche colonization plants", "type": "novelty", "rationale": "Searches for cellular-resolution mapping of microbial colonization in planta, a key proposed outcome." }, { "query": "plant cell-type specific pathogen response transcriptomics", "type": "novelty", "rationale": "Tests whether cell-type-resolved plant immune responses to pathogens have already been reported." }, { "query": "scRNA-seq infected tissue dual plant microbial transcripts", "type": "evidence", "rationale": "Looks for methodological evidence that droplet-based scRNA-seq can capture both host and microbial transcripts from infected tissue." }, { "query": "spatial transcriptomics microbial effector gene expression", "type": "evidence", "rationale": "Seeks evidence that spatially resolved microbial gene expression (effectors) can be detected in situ." }, { "query": "protoplast nuclei isolation infected plant scRNA-seq", "type": "evidence", "rationale": "Targets technical feasibility evidence for protoplasting/nuclei isolation from infected plant tissues for scRNA-seq." }, { "query": "computational assignment microbial reads plant cell clusters", "type": "evidence", "rationale": "Looks for computational methods integrating microbial reads with plant cell cluster assignments." }, { "query": "single-cell spatial transcriptomics tumor heterogeneity niche", "type": "cross_domain", "rationale": "Provides the cancer precedent (tumor subpopulations and spatial niches) that the hypothesis proposes to transfer to plants." }, { "query": "spatial transcriptomics neural invasion leading edge", "type": "cross_domain", "rationale": "Directly targets the TGFBI+ Schwann cell neural invasion analogy cited as the transfer template." }, { "query": "single-cell atlas host-pathogen interface", "type": "cross_domain", "rationale": "Searches for cross-domain precedents (e.g., animal infection models) of single-cell atlases at host-pathogen interfaces." }, { "query": "MERFISH Visium tissue spatial mapping infection", "type": "cross_domain", "rationale": "Checks interdisciplinary precedents of MERFISH/Visium platforms applied to map infection in tissue sections." } ] }, "sharpened": { "title": "Single-cell and spatial transcriptomics as a dual-species tool for mapping plant cell-type responses and microbial effector expression during infection", "formal_statement": "If dual-species scRNA-seq and spatial transcriptomics are applied to infected plant tissues with calibrated spike-in and computational decontamination, then microbial reads will be assignable to plant cell clusters at a false-positive rate ≤5% and microbial effector transcripts will show spatially structured expression (Moran's I ≥0.3) within colonized niches, because cell-type-resolved capture preserves both host and microbial transcripts in situ.", "independent_variables": [ { "name": "pathogen inoculation treatment", "type": "categorical", "range": "mock / wild-type pathogen / non-replicative mutant / heat-killed pathogen", "unit": "treatment level" }, { "name": "spatial sequencing depth per spot", "type": "continuous", "range": "5000-50000", "unit": "UMI per spot" }, { "name": "microbial spike-in load", "type": "continuous", "range": "0-10", "unit": "% of total transcripts" }, { "name": "MOI (multiplicity of infection)", "type": "continuous", "range": "0.01-10", "unit": "CFU per plant cell" }, { "name": "replicate number", "type": "ordinal", "range": "3-8", "unit": "biological replicates" } ], "dependent_variables": [ { "name": "Spearman correlation between microbial UMI density per spot and plant cell-type immune score", "type": "continuous", "expected_direction": "increase", "unit": "rho (dimensionless)" }, { "name": "log2 enrichment ratio of microbial effector transcripts in colonized vs non-colonized cell clusters", "type": "continuous", "expected_direction": "increase", "unit": "log2 ratio" }, { "name": "false-positive rate of microbial read assignment to plant cells", "type": "continuous", "expected_direction": "decrease", "unit": "proportion" }, { "name": "Moran's I of effector transcript spatial distribution", "type": "continuous", "expected_direction": "increase", "unit": "dimensionless" }, { "name": "number of plant cell-type-specific differentially expressed genes (FDR<0.05, |log2FC|>1)", "type": "continuous", "expected_direction": "increase", "unit": "count" } ], "proposed_mechanism": { "causal_chain": [ "Step 1: Protoplast or nuclei isolation from infected tissue preserves both plant and microbial transcripts; capture efficiency is quantified by spike-in controls.", "Step 2: Droplet-based scRNA-seq or spatial barcoding captures host and microbial transcripts; computational decontamination (SoupX/DecontX) removes ambient microbial RNA.", "Step 3: Microbial reads are assigned to plant cell clusters via co-occurrence within droplets/spots; assignment accuracy is validated against calibrated MOI spike-ins and non-replicative mutant controls.", "Step 4: Spatial normalization corrects collinearity between microbial density and sequencing depth; residuals are tested for spatial autocorrelation (Moran's I) to confirm non-artifactual structure.", "Step 5: Effector transcripts expressed in colonized niches are identified as spatially variable genes with enrichment in colonized vs non-colonized clusters." ], "key_assumptions": [ "Microbial transcripts are captured with sufficient efficiency (≥0.1% of total reads) to detect effector expression without amplification bias dominating.", "Computational decontamination removes ≥90% of ambient microbial RNA without removing genuinely intracellular transcripts.", "Plant cell-type clusters are not confounded by protoplasting-induced stress responses (verified by stress-gene score <10% of variance).", "Spatial normalization fully removes the correlation between microbial density and sequencing depth (residual Pearson r <0.1)." ], "known_unknowns": [ "Whether microbial transcripts from intracellular pathogens are captured at rates comparable to plant transcripts in droplet-based assays.", "Whether MERFISH probe sets can be designed for unbiased discovery of uncharacterized effector genes.", "The extent to which zero-inflation in spatial data inflates false-positive effector assignments." ] }, "falsifiable_predictions": [ { "prediction": "Microbial UMI density per spatial spot correlates positively with plant cell-type immune activation score after spatial normalization.", "quantitative_bound": "Spearman rho ≥0.6 (95% CI lower bound ≥0.45) across ≥500 spots, with Monte Carlo power ≥0.8 under zero-inflation (π0=0.3), depth 5000-50000 UMI, and inter-replicate CV=0.25.", "measurement_method": "Spatial transcriptomics (10x Visium) with matched scRNA-seq reference; immune score computed as module score of 50 defense genes; spatial normalization by depth-residual regression.", "null_hypothesis": "H0: rho ≤0.3 between microbial density and immune score after normalization.", "statistical_test": "Spearman correlation with spatial permutation (999 permutations) and Moran's I on residuals; alpha=0.05, two-sided." }, { "prediction": "Microbial effector transcripts are enriched in colonized vs non-colonized plant cell clusters.", "quantitative_bound": "log2 enrichment ratio ≤-1 (i.e., ≥2-fold depletion in non-colonized) for ≥5 effector candidates, with false discovery rate <0.05; power ≥0.8 by Monte Carlo simulation incorporating zero-inflation and inter-replicate variability.", "measurement_method": "Dual-species scRNA-seq with computational decontamination (SoupX/DecontX); effector candidates defined as microbial genes with ≥10 UMIs in colonized clusters; enrichment tested by Wilcoxon rank-sum.", "null_hypothesis": "H0: log2 enrichment ratio ≥0 (no enrichment) for effector candidates.", "statistical_test": "Wilcoxon rank-sum test with Benjamini-Hochberg FDR correction; alpha=0.05." }, { "prediction": "The false-positive rate of microbial read assignment to plant cells is ≤5% under calibrated spike-in and non-replicative mutant controls.", "quantitative_bound": "False-positive proportion ≤0.05 (95% CI upper bound ≤0.08) estimated from permutation of ambient transcripts and non-replicative mutant infection.", "measurement_method": "Spike-in of infected cells at MOI 0.1-10; infection with non-replicative mutant; non-infected tissue supplemented with exogenous microbial transcripts; permutation of ambient transcript labels.", "null_hypothesis": "H0: false-positive rate ≥0.10.", "statistical_test": "Binomial exact test against 0.10 threshold; alpha=0.05, one-sided." }, { "prediction": "Effector transcripts show spatially structured expression within colonized niches.", "quantitative_bound": "Moran's I ≥0.3 (p<0.01 by spatial permutation) for ≥3 effector genes, after correcting for sequencing depth collinearity.", "measurement_method": "Spatial transcriptomics with Moran's I computed on depth-residualized effector UMI counts; spatial permutation (999 randomizations) preserving spot adjacency.", "null_hypothesis": "H0: Moran's I ≤0.1 for effector transcripts.", "statistical_test": "Moran's I permutation test; alpha=0.01, one-sided." } ], "boundary_conditions": [ { "condition": "Microbial load must be ≥0.1% of total transcripts per spot/droplet for reliable detection.", "justification": "Below this threshold, sampling noise and zero-inflation dominate, making effector enrichment estimates unreliable." }, { "condition": "Protoplasting duration must be ≤30 min at 25°C to avoid stress-response confounds.", "justification": "Longer protoplasting induces cell-wall and oxidative stress genes that mask infection-specific responses." }, { "condition": "Spatial sequencing depth must be ≥5000 UMI per spot.", "justification": "Lower depth inflates zero-inflation and reduces power for Moran's I below 0.5." }, { "condition": "Computational decontamination must remove ≥90% of ambient microbial RNA (estimated by empty-droplet controls).", "justification": "Insufficient decontamination inflates false-positive assignment rates above the 5% threshold." }, { "condition": "MERFISH probe design is limited to known effector sequences; unbiased discovery requires sequencing-based ST.", "justification": "MERFISH cannot discover uncharacterized effector genes, restricting its use to hypothesis-testing rather than discovery." } ], "theoretical_framework": "Dual-species single-cell and spatial transcriptomics (methodological transfer from cancer invasion biology to plant pathology), integrated with niche colonization theory and effector-triggered immunity frameworks." }, "protocol": { "protocol_title": "Dual-species scRNA-seq and spatial transcriptomics for mapping plant cell-type responses and microbial effector expression during infection — 3-phase validation protocol", "overall_timeline": "10-18 months", "overall_budget_estimate": "€25k-120k", "phases": [ { "phase_number": 1, "phase_name": "In Silico Validation", "objective": "Determine, before any wet-lab spend, whether (a) published dual-species plant-microbe scRNA/ST datasets contain microbial reads at ≥0.1% of total, (b) decontamination (SoupX/DecontX) can reach ≥90% ambient removal without erasing intracellular signal, and (c) the proposed statistical power (Spearman rho≥0.6, Moran's I≥0.3, FPR≤5%) is achievable under realistic zero-inflation and depth. Kill the hypothesis cheaply if microbial capture or decontamination fails in silico.", "methodology": "1) Harvest public dual-species datasets: 10x Visium and scRNA-seq from Arabidopsis-Pseudomonas, rice-Magnaporthe, tomato-Pseudomonas, Populus nuclei (GEO/SRA/ENA; E-MTAB, PRJNA). 2) Re-quantify with a dual-species reference (STARsolo --soloFeatures Gene Velocyto, or CellRanger multi with concatenated plant+microbe transcriptome). 3) Compute per-droplet/per-spot microbial UMI fraction; test whether ≥0.1% is observed in infected vs mock. 4) Run SoupX (autoEstCont) and DecontX on each dataset; measure ambient removal fraction and retention of spike-in-simulated intracellular transcripts. 5) Build a simulator (Splatter + custom zero-inflation π0=0.3, depth 5k-50k UMI, CV=0.25, MOI 0.01-10) to Monte-Carlo estimate power for Spearman rho≥0.6, Moran's I≥0.3 (999 permutations), and binomial FPR≤5%. 6) Simulate depth-collinearity and test whether depth-residual regression (Pearson r<0.1) restores Moran's I signal.", "required_resources": { "equipment": [ "Linux HPC node (≥64 GB RAM, 16 cores) or cloud (AWS/GCP)" ], "software": [ "CellRanger 7+/STARsolo", "SoupX", "DecontX (celda)", "Seurat v5", "Scanpy", "Splatter", "spdep/ape (Moran's I)", "SPARK-X", "lme4", "powerlmm" ], "datasets": [ "GEO dual-species plant-microbe scRNA-seq (e.g. GSE-series from 2023-2024 atlases)", "10x Visium public plant infection datasets", "Mock/empty-droplet controls from same studies", "Custom concatenated plant+microbe transcriptome (e.g. Arabidopsis TAIR10 + Pseudomonas DC3000)" ], "competences": [ "Bioinformatics (R/Python)", "Spatial statistics", "Power analysis" ], "estimated_cost": "€0-2000", "estimated_duration": "4-8 weeks" }, "expected_outputs": [ "Output 1: Table of observed microbial UMI fraction per dataset (infected vs mock)", "Output 2: SoupX/DecontX removal efficiency and intracellular retention estimates", "Output 3: Power curves for Spearman rho, Moran's I, and FPR under simulated zero-inflation", "Output 4: Go/no-go memo with parameter estimates for Phase 2 design" ], "success_criteria": [ { "metric": "Microbial UMI fraction in infected public datasets", "threshold": "≥0.1% of total UMIs in ≥1 dataset", "measurement": "STARsolo/CellRanger multi quantification" }, { "metric": "Decontamination ambient removal", "threshold": "≥90% of simulated ambient reads removed", "measurement": "SoupX/DecontX on spike-in simulations" }, { "metric": "Simulated power for Spearman rho≥0.6", "threshold": "≥0.8 at n≥500 spots, depth 5k-50k UMI, π0=0.3", "measurement": "Monte Carlo (1000 sims) with Splatter" }, { "metric": "Simulated power for Moran's I≥0.3", "threshold": "≥0.8 at n≥500 spots after depth-residualization", "measurement": "spdep permutation power simulation" }, { "metric": "Simulated FPR control", "threshold": "≤5% under non-replicative mutant / ambient permutation", "measurement": "Binomial simulation" } ], "go_nogo_decision": { "go_if": "≥1 public dataset shows microbial UMI ≥0.1%, decontamination removes ≥90% ambient with <20% intracellular loss, and simulated power ≥0.8 for at least 2 of 4 primary predictions", "nogo_if": "No public dataset reaches 0.1% microbial UMI OR decontamination cannot separate ambient from intracellular (intracellular loss >50%) OR simulated power <0.5 for all predictions", "pivot_if": "Microbial capture is marginal (0.05-0.1%) — pivot to targeted enrichment (microbe-specific capture probes or 16S/ITS amplicon + host scRNA) before Phase 2" }, "risks": [ { "risk": "Public datasets lack matched mock/empty-droplet controls, biasing decontamination estimates", "probability": "high", "mitigation": "Use simulated ambient from non-infected tissue + in-silico spike-in; supplement with one public empty-droplet dataset" }, { "risk": "Zero-inflation model mis-specified, over- or under-estimating power", "probability": "medium", "mitigation": "Fit π0 empirically from public data; run sensitivity across π0=0.1-0.5" }, { "risk": "Species transcriptome concatenation causes multi-mapping", "probability": "medium", "mitigation": "Use STARsolo --soloMultiMappers EM; filter conserved rRNA/chloroplast genes" } ] }, { "phase_number": 2, "phase_name": "Minimal Experimental Validation", "objective": "Physically test the central mechanism — that microbial transcripts are captured in droplets/spots at sufficient rate and can be assigned to plant cell clusters at FPR≤5% — using the smallest possible experiment: one susceptible plant genotype, one pathogen, one timepoint, with calibrated spike-in and non-replicative mutant controls.", "methodology": "1) Plant/pathogen system: Arabidopsis thaliana Col-0 + Pseudomonas syringae pv. tomato DC3000 (or rice-Magnaporthe if lab-equipped); include DC3000 ΔhrcC (non-replicative/T3SS-deficient) and heat-killed controls. 2) Inoculate leaves by syringe infiltration at MOI 0.1, 1, 10 (CFU/cell equivalents); harvest at 24 hpi. 3) Protoplast in ≤30 min at 25°C (enzyme mix in mannitol buffer) OR nuclei isolation (Populus protocol adapted) — run both arms to compare capture. 4) Spike-in: add known quantities of in-vitro-transcribed microbial effector transcripts (e.g. avrPto, hopAI1) and plant control transcripts at 0, 0.1, 1, 10% of total before droplet encapsulation. 5) 10x Genomics 3' scRNA-seq (one lane per condition, ~5000 cells target); include empty-droplet/ambient controls. 6) Quantify with CellRanger multi (concatenated Arabidopsis+DC3000 reference). 7) Run SoupX/DecontX; compute microbial UMI fraction, FPR by permutation of ambient labels and by ΔhrcC vs WT contrast. 8) Cluster plant cells (Seurat), assign microbial reads by droplet co-occurrence, compute log2 enrichment of effectors in colonized vs non-colonized clusters (Wilcoxon + BH FDR).", "required_resources": { "equipment": [ "10x Genomics Chromium Controller + 3' kit", "Bioanalyzer/TapeStation", "Illumina NextSeq or NovaSeq (shared)", "Protoplasting/nuclei isolation setup", "Plant growth chamber (BSL-2 for DC3000)" ], "software": [ "CellRanger multi", "SoupX", "DecontX", "Seurat v5", "edgeR/DESeq2", "scDblFinder" ], "datasets": [ "In-house scRNA-seq (mock, WT, ΔhrcC, heat-killed × MOI 0.1/1/10)", "Spike-in calibration curves", "Empty-droplet ambient profiles" ], "competences": [ "Plant pathology (BSL-2)", "Protoplasting/nuclei isolation", "10x library prep", "Bioinformatics" ], "estimated_cost": "€8k-15k", "estimated_duration": "2-3 months" }, "expected_outputs": [ "Output 1: Microbial UMI fraction per condition and MOI", "Output 2: FPR estimate with 95% CI from permutation and ΔhrcC contrast", "Output 3: Effector log2 enrichment in colonized vs non-colonized clusters", "Output 4: Protoplast vs nuclei capture comparison", "Output 5: Decontamination retention/removal metrics" ], "success_criteria": [ { "metric": "Microbial UMI fraction (WT, MOI 1-10)", "threshold": "≥0.1% of total UMIs", "measurement": "CellRanger multi counts" }, { "metric": "FPR of microbial assignment", "threshold": "≤5% (95% CI upper ≤8%)", "measurement": "Binomial exact test vs 0.10, permutation of ambient labels + ΔhrcC contrast" }, { "metric": "Effector enrichment", "threshold": "log2 ratio ≤-1 for ≥3 candidates, FDR<0.05", "measurement": "Wilcoxon rank-sum + BH" }, { "metric": "Protoplasting stress confound", "threshold": "Stress-gene score <10% of variance", "measurement": "PCA variance decomposition (cell-wall/oxidative stress module)" }, { "metric": "Decontamination", "threshold": "≥90% ambient removed, <20% intracellular loss", "measurement": "SoupX/DecontX vs spike-in" } ], "go_nogo_decision": { "go_if": "Microbial UMI ≥0.1%, FPR≤5% (CI upper ≤8%), and ≥3 effectors enriched (log2≤-1, FDR<0.05)", "nogo_if": "Microbial UMI <0.05% at MOI 10 OR FPR>10% (CI lower >8%) OR no effector enrichment in WT vs ΔhrcC", "pivot_if": "Capture marginal but FPR controlled — pivot to targeted microbial capture (custom 10x probe panel or microbe-specific RT primers) before Phase 3; if protoplasting stress confounds, switch to nuclei-based workflow" }, "risks": [ { "risk": "Protoplasting induces stress masking infection signal", "probability": "high", "mitigation": "Parallel nuclei arm; ≤30 min digestion; stress-gene score QC gate" }, { "risk": "Low microbial capture in droplets", "probability": "medium", "mitigation": "MOI escalation to 10; spike-in calibration; targeted capture fallback" }, { "risk": "Ambient RNA inflates FPR", "probability": "medium", "mitigation": "Empty-droplet controls; SoupX autoEstCont; ΔhrcC contrast as biological FPR anchor" }, { "risk": "Batch effect across MOI/conditions", "probability": "medium", "mitigation": "Randomize lanes; include mock in each batch; Harmony/scVI integration" } ] }, { "phase_number": 3, "phase_name": "Full Experimental Protocol", "objective": "Rigorously validate all four falsifiable predictions at publishable standard: spatial correlation of microbial density with immune score (Spearman rho≥0.6), effector enrichment (log2≤-1), FPR≤5%, and spatially structured effector expression (Moran's I≥0.3), across ≥3 biological replicates and ≥2 timepoints, with spatial transcriptomics (10x Visium) matched to scRNA-seq reference.", "methodology": "1) Expand to 3-8 biological replicates × 2-3 timepoints (12, 24, 48 hpi) × 4 treatments (mock, WT, ΔhrcC, heat-killed) × MOI 0.1/1/10. 2) Paired design: same tissue split for (a) 10x Visium spatial (depth 5k-50k UMI/spot, ≥500 spots/section) and (b) 10x scRNA-seq reference (protoplast + nuclei arms). 3) Spike-in calibration at 0/0.1/1/10% in every batch. 4) Spatial normalization: regress microbial UMI density on sequencing depth; retain residuals with Pearson r<0.1; compute Moran's I (999 spatial permutations, spdep/SPARK-X) on depth-residualized effector counts. 5) Immune score = 50-gene defense module (Seurat AddModuleScore); Spearman rho with microbial density after normalization, 999 spatial permutations. 6) Effector candidates: microbial genes ≥10 UMIs in colonized clusters; Wilcoxon + BH FDR; require replication across ≥2 timepoints. 7) FPR: permutation of ambient labels + ΔhrcC + heat-killed contrasts; binomial exact test vs 0.10. 8) Optional MERFISH validation of ≥3 known effectors (hypothesis-testing only, per boundary condition). 9) Pre-register analysis plan (OSF) before unblinding.", "required_resources": { "equipment": [ "10x Visium + Chromium Controller", "Illumina NovaSeq (≥2 lanes)", "Cryostat for tissue sectioning", "BSL-2 plant growth facility", "MERFISH setup (optional, shared)" ], "software": [ "Space Ranger", "CellRanger multi", "SoupX/DecontX", "Seurat v5 + SPARK-X", "spdep/ape", "Splatter (power re-check)", "Harmony/scVI", "edgeR/DESeq2" ], "datasets": [ "In-house Visium + scRNA-seq (≥3 reps × 3 timepoints × 4 treatments)", "Spike-in calibration series", "Empty-droplet and no-tissue spot controls", "Pre-registered analysis plan (OSF)" ], "competences": [ "Plant pathology BSL-2", "Spatial transcriptomics (Visium)", "scRNA-seq", "Spatial statistics", "Bioinformatics", "Study design/power" ], "estimated_cost": "€60k-120k", "estimated_duration": "10-18 months" }, "expected_outputs": [ "Output 1: Spearman rho (95% CI) microbial density vs immune score after normalization", "Output 2: Effector log2 enrichment with FDR across timepoints", "Output 3: FPR estimate with 95% CI", "Output 4: Moran's I for ≥3 effectors with spatial permutation p-values", "Output 5: Replicated cell-type-specific DEG counts (FDR<0.05, |log2FC|>1)", "Output 6: Publishable dataset + pre-registered analysis" ], "success_criteria": [ { "metric": "Spearman rho microbial density vs immune score", "threshold": "≥0.6 (95% CI lower ≥0.45), ≥500 spots, spatial permutation p<0.05", "measurement": "Spearman + 999 spatial permutations" }, { "metric": "Effector enrichment", "threshold": "log2≤-1 for ≥5 candidates, FDR<0.05, replicated in ≥2 timepoints", "measurement": "Wilcoxon + BH" }, { "metric": "FPR", "threshold": "≤5% (95% CI upper ≤8%)", "measurement": "Binomial exact test vs 0.10" }, { "metric": "Moran's I effector spatial structure", "threshold": "≥0.3 (p<0.01) for ≥3 effectors after depth-residualization", "measurement": "spdep/SPARK-X 999 permutations" }, { "metric": "Cell-type-specific DEGs", "threshold": "≥50 DEGs per major cluster, FDR<0.05, |log2FC|>1", "measurement": "edgeR/DESeq2 pseudobulk" }, { "metric": "Depth-collinearity removal", "threshold": "Residual Pearson r<0.1", "measurement": "Regression diagnostics" } ], "go_nogo_decision": { "go_if": "All 4 primary predictions met with replication across ≥2 timepoints and ≥3 biological replicates", "nogo_if": "Any primary prediction fails with 95% CI excluding the bound (e.g. rho CI upper <0.45, FPR CI lower >8%, Moran's I<0.1)", "pivot_if": "Partial success (e.g. rho met but Moran's I fails) — pivot to targeted effector panel + higher-depth spatial (Visium HD or Slide-seq) and report as method-limitation paper" }, "risks": [ { "risk": "Insufficient biological replication inflates variance beyond CV=0.25", "probability": "medium", "mitigation": "Power re-check in Phase 1 simulator; pre-specify n=5 minimum; adaptive interim analysis" }, { "risk": "Spatial depth collinearity not fully removed", "probability": "medium", "mitigation": "Depth-residual regression + sensitivity analysis with downsampling to common depth" }, { "risk": "Effector annotation incomplete for uncharacterized genes", "probability": "high", "mitigation": "Sequencing-based ST for discovery; MERFISH only for known effectors (per boundary condition)" }, { "risk": "Batch/lane effects across timepoints", "probability": "medium", "mitigation": "Randomize timepoints across lanes; include mock per batch; Harmony/scVI" }, { "risk": "Zero-inflation inflates Moran's I false positives", "probability": "medium", "mitigation": "SPARK-X zero-inflated model; permutation-based null; sensitivity to π0" } ] } ], "phase_1_quick_start": { "can_start_today": true, "first_action": "Download 2-3 public dual-species plant-microbe scRNA-seq/Visium datasets (GEO/SRA) and run STARsolo with a concatenated Arabidopsis+DC3000 reference to compute microbial UMI fraction in infected vs mock — this single number decides whether the hypothesis survives week 1.", "tools_needed": [ "STARsolo or CellRanger multi", "SoupX", "DecontX", "Seurat v5", "Splatter", "spdep" ], "open_data_sources": [ "GEO (ncbi.nlm.nih.gov/geo)", "SRA/ENA", "E-MTAB (ArrayExpress)", "Zenodo dual-species atlases", "10x public Visium datasets" ] } }, "panel": { "reviews": [ { "reviewer_persona": "methodologist", "overall_score": 8.2, "verdict": "accept", "strengths": [ "Le protocole est structuré en trois phases avec des critères GO/NO-GO explicites et des seuils quantitatifs, ce qui permet une évaluation objective et progressive de la faisabilité avant tout engagement financier lourd.", "La validation in silico (Phase 1) utilise des jeux de données publics et un simulateur Monte-Carlo pour estimer la puissance statistique sous zero-inflation et profondeur variable, ce qui est une pratique exemplaire pour tester la robustesse des prédictions avant le laboratoire.", "Les contrôles expérimentaux incluent un mutant non réplicatif (ΔhrcC) et un traitement par chaleur tue, ainsi que des spike-ins calibrés, ce qui permet de distinguer les transcrits microbiens intracellulaires des transcrits ambiants et d'estimer le taux de faux positifs.", "Le plan d'analyse prévoit une correction de la colinéarité avec la profondeur de séquençage (régression sur les résidus) et l'utilisation de permutations spatiales pour Moran's I, ce qui renforce la validité interne des inférences spatiales.", "La puissance statistique est estimée a priori par simulation pour chaque prédiction principale, avec des seuils de puissance ≥0.8 et des bornes de confiance, ce qui est rare et mérite d'être souligné." ], "weaknesses": [ "La puissance statistique pour la prédiction 3 (FPR≤5%) repose sur une estimation binomiale exacte, mais le protocole ne détaille pas le nombre de pseudo-réplicats ou de permutations nécessaire pour atteindre une précision suffisante (par exemple, pour un FPR de 5% avec une borne supérieure de 8%, il faut un nombre élevé d'observations indépendantes, non spécifié).", "Le risque de biais de sélection des gènes effecteurs est important : les candidats sont définis comme ayant ≥10 UMIs dans les clusters colonisés, ce qui peut exclure des effecteurs à faible expression mais biologiquement pertinents, et le protocole ne propose pas de contrôle pour ce biais de sélection.", "La Phase 2 utilise un seul génotype de plante et un seul pathogène, ce qui limite la généralité des résultats et peut introduire un biais de confirmation si l'équipe est déjà familière avec ce système ; aucune analyse de sensibilité à d'autres systèmes n'est prévue avant la Phase 3.", "Le protocole ne discute pas explicitement du biais de publication potentiel : les résultats négatifs (par exemple, absence de structure spatiale) ne sont pas clairement destinés à être publiés, ce qui pourrait encourager des analyses sélectives.", "La mesure de l'expression des effecteurs microbiens par scRNA-seq et spatial transcriptomics peut être confondue par la présence d'ARN microbien extracellulaire non infectant ; le protocole tente de contrôler cela avec le mutant ΔhrcC et la chaleur tue, mais ne quantifie pas la contribution relative de l'ARN ambiant versus intracellulaire dans les spots, ce qui laisse une incertitude sur la validité de la mesure." ], "critical_questions": [ "Comment le protocole garantit-il que le nombre de réplicats biologiques (3-8) et le nombre de spots (≥500) sont suffisants pour atteindre une puissance de 0.8 pour la prédiction 1 (Spearman rho≥0.6) sous une zero-inflation de π0=0.3 et un CV inter-réplicats de 0.25 ? Une analyse de puissance plus détaillée avec des scénarios de variance est-elle prévue ?", "Quels contrôles supplémentaires sont prévus pour distinguer les transcrits microbiens intracellulaires des transcrits ambiants adsorbés à la surface des cellules, au-delà du mutant ΔhrcC et de la chaleur tue ? Par exemple, un traitement à la RNase avant lyse ou une comparaison avec des souches déficientes en sécrétion ?", "Comment le protocole gère-t-il le biais de sélection des effecteurs candidats (≥10 UMIs) ? Une analyse de sensibilité avec des seuils plus bas ou une approche bayésienne pour les gènes à faible expression est-elle envisagée ?", "Le plan d'analyse pré-enregistré (OSF) inclut-il explicitement les critères d'exclusion des spots ou des cellules de faible qualité, et comment ces exclusions pourraient-elles affecter le taux de faux positifs et la puissance statistique ?" ], "recommendation": "Le protocole est globalement rigoureux, avec une approche progressive et des contrôles bien pensés. Je recommande d'accepter sous réserve que les auteurs renforcent la justification de la puissance statistique pour la prédiction 3 (FPR) en précisant le nombre d'observations indépendantes et en ajoutant une analyse de sensibilité pour le biais de sélection des effecteurs. Il serait également utile de discuter explicitement du biais de publication et de préciser les critères d'exclusion des données de faible qualité dans le pré-enregistrement.", "confidence": 0.85 }, { "reviewer_persona": "domain_expert", "overall_score": 5.8, "verdict": "weak_accept", "strengths": [ "Le chaînage causal est globalement bien structuré et intègre des contrôles quantitatifs explicites (spike-in calibré, taux de faux positifs ≤5%, seuil Moran's I ≥0.3, résidu de Pearson r <0.1), ce qui dépasse la plupart des propositions dual-species qui restent qualitatives. Cette formalisation en critères falsifiables est un réel atout méthodologique.", "La distinction entre décontamination computationnelle (SoupX/DecontX) et validation par mutants non-réplicatifs est pertinente : elle attaque frontalement le problème de l'ARN microbien ambiant, qui est le point d'échec le plus documenté des approches dual-species en tissu infecté.", "L'identification de l'étape 4 (normalisation spatiale de la colinéarité densité microbienne / profondeur de séquençage) comme verrou statistique est un point fin et rarement explicité dans la littérature ST appliquée aux pathosystèmes ; cela montre une compréhension réelle des artefacts propres au spatial." ], "weaknesses": [ "Le positionnement vs l'état de l'art est le point faible majeur : Nobori et al. (2023, DOI:10.1101/2023.04.10.536170) démontre déjà scRNA-seq + ST time-resolved sur Arabidopsis infecté par Pseudomonas syringae avec cartographie de la distribution du pathogène, et Tang et al. (2023, DOI:10.1101/2023.04.03.535386) assigne déjà des cellules interagissant avec l'hyphe. La proposition se présente comme un transfert méthodologique alors que la preuve de concept existe déjà ; la nouveauté résiduelle (effector mapping in situ à résolution cellulaire) n'est pas isolée ni défendue contre ces travaux.", "L'hypothèse d'efficacité de capture microbienne ≥0.1% des reads totaux est plausible pour des bactéries intracellulaires à forte charge, mais très fragile pour des champignons filamenteux ou des effecteurs exprimés transitoirement à faible niveau ; aucun calcul de puissance n'est fourni reliant charge microbienne, MOI, profondeur de séquençage et détectabilité d'un effecteur donné. Le seuil de 0.1% apparaît comme un chiffre posé a priori plutôt que dérivé.", "La suppression de ≥90% de l'ARN ambiant par DecontX/SoupX sans éliminer les transcrits véritablement intracellulaires est une affirmation non étayée : ces méthodes reposent sur des profils de référence ambient et échouent précisément quand le signal biologique est spatialement corrélé à la contamination (cas typique des niches colonisées). Le mécanisme ne propose pas de contrôle orthogonal (ex. FISH sur effecteur candidat) pour trancher.", "L'assignation des reads microbiens aux clusters cellulaires par co-occurrence dans les droplets (étape 3) confond co-encapsulation et infection réelle ; le contrôle par mutants non-réplicatifs valide la spécificité biologique mais pas la résolution d'assignation. Aucune estimation du taux de doublets hôte-microbe ni de la fuite de transcrits entre cellules adjacentes n'est proposée.", "La base bibliographique est correcte mais asymétrique : elle cite abondamment les atlas scRNA-seq/ST plantes-pathogènes récents et omet la littérature méthodologique critique sur la décontamination en single-cell (notamment les travaux montrant les limites de SoupX/DecontX sur données à faible charge), ainsi que les travaux de dual-species RNA-seq fongique (ex. Dual RNA-seq de pathosystèmes foliaires) qui fixent le plafond de détectabilité des effecteurs." ], "critical_questions": [ "Quel calcul de puissance relie la charge microbienne attendue (CFU ou biomasse fongique par cellule), le MOI, la profondeur de séquençage par cellule/spot et la probabilité de détecter un transcrit d'effecteur à expression faible (ex. <10 copies par cellule infectée) ? Sans ce calcul, le seuil de 0.1% n'est pas justifié.", "Comment le mécanisme distingue-t-il un transcrit d'effecteur réellement intracellulaire d'un transcrit adsorbé à la surface cellulaire ou relargué dans l'apoplaste, sachant que DecontX/SoupX modélisent l'ambiant de manière globale et non compartimentée ? Un contrôle orthogonal (RNAscope sur 2-3 effecteurs prédits) est-il prévu ?", "La validation par mutants non-réplicatifs contrôle-t-elle vraiment l'assignation, ou seulement l'absence de signal biologique ? Comment mesure-t-on le taux de doublets hôte-microbe et la fuite de transcrits entre spots adjacents en spatial barcoding ?", "Le seuil Moran's I ≥0.3 est-il calibré sur des simulations de données zero-inflated avec la profondeur de séquençage observée dans ce type d'expérience, ou est-il emprunté à la littérature ST de tissus non infectés ? La distribution nulle de Moran's I sous zero-inflation spatiale est-elle établie pour ce design ?", "Quelle est la différence opérationnelle entre cette proposition et Nobori et al. 2023, au-delà de l'ajout d'effecteur mapping ? Si l'effecteur mapping est le vrai delta, pourquoi ne pas le formuler comme hypothèse centrale plutôt que comme étape 5 d'une chaîne déjà démontrée ?" ], "recommendation": "Je recommande un weak_accept : la proposition est méthodologiquement sérieuse et formalise des critères falsifiables utiles, mais son positionnement vs Nobori et al. 2023 et Tang et al. 2023 doit être réécrit pour isoler le delta réel (cartographie spatiale des effecteurs microbiens à résolution cellulaire) et le défendre explicitement. Il faut impérativement ajouter un calcul de puissance reliant charge microbienne, profondeur et détectabilité des effecteurs, ainsi qu'un contrôle orthogonal de décontamination (FISH/RNAscope sur effecteurs candidats) pour rendre l'hypothèse testable au-delà de SoupX/DecontX. En l'état, la chaîne causale est plausible mais la nouveauté est surévaluée et le verrou de décontamination spatialement corrélée n'est pas résolu.", "confidence": 0.78 }, { "reviewer_persona": "contrarian", "overall_score": 3.5, "verdict": "reject", "strengths": [ "L'architecture expérimentale est ambitieuse et intègre des contrôles de calibration (spike-in, mutant non réplicatif, permutation des transcrits ambiants) qui montrent une réelle conscience du problème de contamination croisée, souvent ignoré dans la littérature dual-species.", "La formalisation des hypothèses nulles et des bornes d'effet (Moran's I ≥0.3, FPR ≤5%, log2 enrichment ≤-1) est inhabituellement rigoureuse et rend l'hypothèse falsifiable, ce qui est louable." ], "weaknesses": [ "FAIL REASON #1: Le maillon Step 1 est le point de rupture le plus probable : l'isolement de protoplastes ou de noyaux à partir de tissus infectés détruit ou lyse sélectivement les cellules microbiennes et les structures intracellulaires (haustoria, arbuscules), de sorte que la fraction de transcrits microbiens réellement intracellulaires capturée sera très inférieure au seuil de 0,1 % revendiqué ; les transcrits microbiens détectés seront majoritairement ambiants ou extracellulaires, et la décontamination SoupX/DecontX ne peut pas distinguer un transcrit intracellulaire authentique d'un transcrit ambiant co-capturé — l'hypothèse d'une décontamination à ≥90 % sans perte des transcrits intracellulaires est non testée et probablement fausse, car les deux populations partagent la même signature de composition.", "FAIL REASON #2: La chaîne causale Step 3 (assignation des reads microbiens aux clusters de cellules végétales par co-occurrence dans les droplets) repose sur une hypothèse de colocalisation non validée : dans un droplet contenant une cellule végétale et plusieurs génomes microbiens, la co-occurrence ne prouve pas l'infection intracellulaire mais seulement la proximité ; le mutant non réplicatif contrôle la réplication mais pas l'adhésion extracellulaire résiduelle, et le spike-in MOI calibré ne peut pas distinguer une vraie assignation d'une contamination de surface — le taux de faux positifs de 5 % est donc structurellement inatteignable, et le test de permutation des labels ambiants ne modélise pas la corrélation spatiale entre densité microbienne et profondeur de séquençage, ce qui garantit un faux positif.", "FAIL REASON #3: La prédiction 1 (Spearman rho ≥0.6 entre densité microbienne et score d'activation immunitaire après normalisation) est un artefact attendu : la densité microbienne et la profondeur de séquençage sont colinéaires par construction (plus de microbes = plus de reads totaux = spots plus profonds), et la régression sur les résidus de profondeur ne supprime pas cette colinéarité si la profondeur est elle-même un médiateur causal de la densité ; de plus, avec π0=0.3 de zéro-inflation et un CV inter-réplicats de 0.25, la puissance de 0.8 annoncée est optimiste et le Moran's I ≥0.3 sur des résidus de profondeur est mécaniquement biaisé vers zéro ou vers un artefact de voisinage, rendant la structure spatiale non interprétable comme biologique." ], "critical_questions": [ "Comment le protocole distingue-t-il, au niveau du droplet, un transcrit microbien intracellulaire authentique d'un transcrit ambiant adsorbé à la surface de la paroi végétale, sachant que SoupX/DecontX estiment tous deux à partir de la même matrice de comptage et ne disposent d'aucun marqueur d'intracellulaire ?", "Quelle est la puissance réelle du test de Moran's I ≥0.3 sur des résidus de profondeur lorsque la densité microbienne explique déjà la majorité de la variance de profondeur, et comment le manuscrit démontre-t-il que le signal résiduel n'est pas un artefact de lissage spatial (autocorrélation induite par la normalisation elle-même) ?", "Le mutant non réplicatif contrôle-t-il réellement l'assignation faussement positive, ou ne fait-il que déplacer le problème vers l'adhésion extracellulaire non réplicative, qui reste co-capturée et non distinguable d'une infection intracellulaire ?" ], "recommendation": "Avant toute revendication de FPR ≤5 %, il faut démontrer expérimentalement, par FISH ou par marquage métabolique (BONCAT/click-chemistry) couplé à la capture, la fraction de transcrits microbiens réellement intracellulaires dans les droplets ; sans cette validation indépendante, les Steps 1 et 3 restent des hypothèses non testées et les prédictions 1, 3 et 4 sont des artefacts de profondeur et de co-occurrence. Je recommande de restreindre l'hypothèse à un système modèle où l'infection intracellulaire est vérifiable par microscopie corrélative, et de rapporter explicitement le taux de transcrits microbiens ambiants mesuré sur tissu non infecté supplémenté, plutôt que de postuler une décontamination à 90 %.", "confidence": 0.82 }, { "reviewer_persona": "industrialist", "overall_score": 6.5, "verdict": "weak_accept", "strengths": [ "Le marché adressable est réel : les acteurs du phyto-protection (Bayer Crop Science, Syngenta, Corteva) et les semenciers (Limagrain, KWS) investissent massivement dans la résistance génétique et les bio-stimulants. Un outil dual-species single-cell/spatial permettrait de cribler des génotypes et des effecteurs microbiens avec une résolution inédite, justifiant un prix de licence élevé (estimation TAM 150-300 M€ pour les services de phénotypage moléculaire en pathologie végétale, CAGR ~12%).", "L'avantage compétitif est double : (1) la barrière IP peut être construite sur les pipelines de décontamination calibrée et les contrôles spike-in, difficiles à répliquer sans savoir-faire ; (2) l'intégration de la théorie de la colonisation de niche et de l'ETI fournit un cadre interprétatif propriétaire que les concurrents (10x Genomics, NanoString) ne proposent pas nativement pour les interactions plante-microbe.", "Le protocole en 3 phases avec kill-switch in silico (Phase 1 à 0-2 k€) est un atout commercial majeur : il permet de valider la faisabilité avant tout engagement lourd, réduisant le risque d'investissement et facilitant le financement par des subventions (Horizon Europe, BBSRC) ou des partenariats industriels." ], "weaknesses": [ "La taille du marché est limitée par la fragmentation des pathosystèmes : chaque couple plante-pathogène nécessite une calibration propre (spike-in, décontamination, référence scRNA-seq). Les coûts de développement par nouvel hôte (60-120 k€ par pathosystème) rendent le modèle économique difficile à scaler sans automatisation poussée, ce qui retarde le ROI.", "La concurrence existante est sous-estimée : les plateformes de spatial transcriptomics (10x Visium, Curio Seeker) et les outils de décontamination (SoupX, DecontX) sont open-source ou peu coûteux. La valeur ajoutée réside dans l'intégration, mais les clients potentiels (académiques et industriels) peuvent internaliser ces pipelines, réduisant la disposition à payer pour un service tiers.", "La timeline de commercialisation est réaliste mais longue : TRL actuel ~3-4 (validation en conditions contrôlées), le passage à un produit commercial (kit ou service) nécessite 24-36 mois après la Phase 3, avec des coûts réglementaires et de validation supplémentaires (normes ISO pour les services de séquençage, par exemple). Le risque d'échec en Phase 3 (prédiction 3 sur le FPR≤5%) reste élevé et pourrait invalider l'ensemble de la proposition de valeur." ], "critical_questions": [ "Quel est le modèle de revenus précis : vente de kits de spike-in calibrés, licence de pipeline logiciel, ou service de profilage clé en main ? Le prix unitaire par échantillon et le volume annuel nécessaire pour atteindre la rentabilité sont-ils compatibles avec la fragmentation du marché ?", "Comment protégez-vous la propriété intellectuelle sur les contrôles spike-in et les seuils de décontamination, sachant que les algorithmes sous-jacents (SoupX, DecontX) sont open-source et que les concurrents peuvent développer des variantes ? Un brevet sur l'application dual-species est-il réellement défendable ?", "Quel est le plan de validation commerciale avec un partenaire industriel (par exemple, un semencier ou un fabricant de produits de protection des plantes) avant la Phase 3, pour sécuriser un premier contrat et réduire le risque de marché ?" ], "recommendation": "Lancer immédiatement la Phase 1 in silico (coût <2 k€) pour confirmer la faisabilité technique et identifier les pathosystèmes les plus prometteurs (ex. Arabidopsis-Pseudomonas, riz-Magnaporthe). En parallèle, démarcher un partenaire industriel (Limagrain, Bayer) pour co-financer la Phase 2 et sécuriser un premier cas d'usage commercial, tout en déposant un brevet sur le pipeline de décontamination calibrée et les contrôles spike-in avant toute publication. Ne pas engager la Phase 3 sans un engagement de principe d'un client payant.", "confidence": 0.65 }, { "reviewer_persona": "funding_strategist", "overall_score": 7.5, "verdict": "weak_accept", "strengths": [ "Le protocole en trois phases avec des critères GO/NO-GO quantitatifs et un investissement initial très faible (≤2 k€ en phase in silico) constitue un atout majeur pour les financeurs : il démontre une gestion rigoureuse du risque et permet d'écarter rapidement les fausses pistes avant tout engagement lourd.", "L'hypothèse combine scRNA-seq et transcriptomique spatiale sur tissus infectés, avec des prédictions falsifiables (FPR≤5%, Moran's I≥0.3, rho≥0.6) et des contrôles mutants (ΔhrcC), ce qui répond aux exigences croissantes de reproductibilité et de rigueur statistique des appels Horizon Europe et ANR.", "Le budget total (25–120 k€) et la durée (10–18 mois) sont parfaitement calibrés pour un appel « tremplin » ou « proof-of-concept », avec un fort effet levier : les résultats de phase 2 peuvent servir de base à une demande ERC ou à un projet collaboratif plus vaste." ], "weaknesses": [ "Le TRL actuel est très bas (TRL 2–3) : il s'agit d'une validation méthodologique, pas encore d'une application translationnelle, ce qui exclut les appels orientés innovation ou maturation technologique et limite l'éligibilité aux programmes de recherche fondamentale.", "Le consortium n'est pas défini et l'hypothèse repose sur un seul système plante-pathogène (non spécifié) ; sans partenaire expert en transcriptomique spatiale et en analyse computationnelle de données dual-species, la crédibilité du plan de gestion des données et de la reproductibilité est fragilisée.", "La charge microbienne attendue (≥0,1% des UMI) est proche des limites de détection des plateformes actuelles, et la décontamination (SoupX/DecontX) reste un verrou méthodologique non résolu à grande échelle ; le risque d'échec en phase 2 est réel et doit être explicitement mitigé dans la proposition." ], "critical_questions": [ "Quel système plante-pathogène précis est ciblé, et dispose-t-on de données publiques ou préliminaires démontrant que la charge microbienne intracellulaire atteint le seuil de 0,1% des UMI dans ce système ?", "Quels partenaires ou collaborations sont envisagés pour la partie transcriptomique spatiale (10x Visium) et pour l'analyse bio-informatique dual-species, et comment le consortium garantit-il l'accès aux plateformes et l'expertise en décontamination ?", "Comment le plan de gestion des données et le partage des jeux de données dual-species seront-ils structurés pour satisfaire aux exigences FAIR et aux politiques de science ouverte des financeurs (Horizon Europe, ANR) ?" ], "recommendation": "Positionner cette hypothèse comme une étude de preuve de concept méthodologique à fort risque et fort gain, en réponse à un appel ANR « Tremplin » ou « PRC » avec un consortium restreint (1–2 partenaires) incluant une plateforme de transcriptomique spatiale. Utiliser les résultats de la phase 1 in silico comme preuve préliminaire dans la lettre d'intention, et prévoir une demande ERC Starting Grant ou Consolidator en phase 3 pour étendre le cadre à plusieurs pathosystèmes. Le narratif doit insister sur la falsifiabilité, la gestion du risque et l'ouverture des données, plutôt que sur des promesses applicatives prématurées.", "confidence": 0.8, "funding_programs": [ { "program": "ANR Appel à projets générique 2026 – PRC (Projet de Recherche Collaborative)", "agency": "Agence Nationale de la Recherche", "fit_score": 0.85, "typical_budget": "300–600 k€ (volet PRC)", "success_rate": "≈20–25% (PRC)", "next_deadline": "Octobre 2025 (lettre d'intention) / Mars 2026 (dossier complet)", "rationale": "L'ANR PRC finance des projets de recherche fondamentale à consortium restreint (2–4 partenaires) sur 36–48 mois, avec un budget compatible avec l'extension du protocole à plusieurs pathosystèmes. La structure en phases GO/NO-GO et les critères quantitatifs de falsifiabilité correspondent aux attentes de l'ANR en matière de rigueur méthodologique et de gestion des risques. Le volet « Tremplin » de l'ANR pourrait également être visé pour la phase 1–2, avant de basculer vers un PRC." }, { "program": "Horizon Europe – HORIZON-CL6-2026-02-BIODIV-01 (ou appel équivalent sur la santé des plantes et les interactions hôte-microbe)", "agency": "Commission Européenne (DG Recherche et Innovation)", "fit_score": 0.7, "typical_budget": "3–5 M€ (projet collaboratif)", "success_rate": "≈15–20% (appels CL6)", "next_deadline": "Février 2026 (appel à propositions)", "rationale": "Les appels du cluster 6 (Alimentation, Bioéconomie, Ressources naturelles, Agriculture et Environnement) financent des projets collaboratifs sur les interactions plante-microbe et la santé des cultures. L'hypothèse s'inscrit dans le thème « outils moléculaires pour la surveillance des pathogènes » et pourrait être intégrée comme work package méthodologique dans un consortium plus large incluant des partenaires européens experts en transcriptomique spatiale et en épidémiologie végétale. Le TRL bas est compensé par le caractère fondamental de l'appel, mais la taille du consortium requise (≥3 pays) nécessite un partenaire coordinateur expérimenté." }, { "program": "ERC Starting Grant 2026 (ou Consolidator Grant 2026)", "agency": "European Research Council", "fit_score": 0.65, "typical_budget": "1,5–2 M€ (Starting) / 2–2,5 M€ (Consolidator)", "success_rate": "≈10–12% (Starting)", "next_deadline": "Octobre 2025 (Starting) / Février 2026 (Consolidator)", "rationale": "L'ERC finance des projets pionniers à haut risque et haut gain portés par un chercheur principal, sans consortium obligatoire. L'hypothèse, par son caractère méthodologique ambitieux (cartographie dual-species à l'échelle de la cellule unique) et sa falsifiabilité, correspond à l'esprit « frontier research » de l'ERC. Cependant, le TRL très bas et l'absence de données préliminaires solides constituent un frein ; une demande ERC serait plus compétitive après obtention des résultats de phase 2 (preuve de concept expérimentale) et avec un CV de PI démontrant une expertise reconnue en transcriptomique spatiale et en interactions plante-microbe." } ] } ], "meta_review": { "consensus_score": 6.3, "verdict": "publish_brief", "key_consensus": [ "Tous les reviewers reconnaissent la rigueur inhabituelle de la formalisation : critères GO/NO-GO quantitatifs, seuils falsifiables (FPR≤5%, Moran's I≥0.3, rho≥0.6), contrôles mutants (ΔhrcC) et spike-ins calibrés. Le methodologist, le domain_expert et même le contrarian saluent cette falsifiabilité comme un atout méthodologique réel.", "Le verrou central identifié de façon convergente est la décontamination de l'ARN microbien ambiant : methodologist, domain_expert et contrarian s'accordent pour dire que SoupX/DecontX ne distinguent pas de manière fiable un transcrit intracellulaire authentique d'un transcrit ambiant co-capturé, et qu'un contrôle orthogonal (FISH/RNAscope, BONCAT) est nécessaire.", "La structure en trois phases avec un investissement initial très faible (≤2 k€) est unanimement perçue comme une force : le methodologist, l'industrialist et le funding_strategist y voient une gestion exemplaire du risque permettant d'écarter les fausses pistes avant engagement lourd.", "Le chaînage causal est jugé plausible et bien structuré par l'ensemble du panel, y compris les sceptiques ; le désaccord porte sur la nouveauté et la robustesse des maillons, non sur la cohérence logique de la chaîne." ], "key_disagreements": [ "Le positionnement vs l'état de l'art est le point de clivage majeur : le domain_expert estime que Nobori et al. (2023) et Tang et al. (2023) ont déjà démontré la preuve de concept dual-species scRNA-seq+ST sur tissus infectés, réduisant la nouveauté résiduelle à l'effecteur mapping in situ, non isolé ni défendu. Le methodologist et le funding_strategist ne soulèvent pas cette antériorité et traitent la proposition comme méthodologiquement neuve.", "La faisabilité du seuil FPR≤5% est contestée de façon frontale : le contrarian (confidence 0.82) soutient que les Steps 1 et 3 rendent ce seuil structurellement inatteignable (lyse sélective des cellules microbiennes, confusion co-occurrence/infection), tandis que le methodologist juge le protocole capable d'atteindre ce seuil moyennant des précisions sur le nombre d'observations indépendantes.", "Le seuil de charge microbienne ≥0.1% des UMI est jugé plausible par le methodologist mais « posé a priori plutôt que dérivé » par le domain_expert, qui exige un calcul de puissance reliant charge microbienne, MOI, profondeur et détectabilité des effecteurs à faible expression.", "La prédiction 1 (Spearman rho≥0.6) est considérée comme un artefact de colinéarité profondeur/densité par le contrarian, alors que le methodologist la valide sous réserve d'une correction de colinéarité par régression sur résidus et de permutations spatiales." ], "critical_path": "Le facteur le plus déterminant est la démonstration expérimentale, par un contrôle orthogonal indépendant (FISH/RNAscope sur effecteurs candidats, ou marquage métabolique BONCAT couplé à la capture), de la fraction de transcrits microbiens réellement intracellulaires capturés dans les droplets/spots. Sans cette validation, les Steps 1 et 3 restent des hypothèses non testées et les prédictions 1, 3 et 4 peuvent être des artefacts de profondeur et de co-occurrence — c'est le point de rupture convergent entre le domain_expert et le contrarian, et la condition sine qua non pour que le seuil FPR≤5% soit crédible.", "final_recommendation": "Le panel reconnaît une proposition méthodologiquement sérieuse, falsifiable et bien calibrée en risque, mais dont la nouveauté est surévaluée face à Nobori et al. (2023) et Tang et al. (2023) et dont le maillon critique — la décontamination de l'ARN ambiant spatialement corrélé — n'est pas résolu. Le brief est publié en l'état à l'itération 2, mais il doit être lu comme une étude de preuve de concept à haut risque : la priorité absolue est d'ajouter un contrôle orthogonal de décontamination et un calcul de puissance reliant charge microbienne, profondeur et détectabilité des effecteurs. Le delta réel (cartographie spatiale des effecteurs à résolution cellulaire) doit être reformulé comme hypothèse centrale plutôt que comme étape 5 d'une chaîne déjà démontrée. Les concerns du contrarian (FAIL REASON #1 et #3) sont documentés et devront être adressés explicitement dans le plan expérimental avant tout engagement en Phase 3.", "brief_quality_gate": true, "revision_guidance": [], "llm_verdict": "publish_brief", "llm_consensus_score": 6.4 } }, "vulgarization_fr": { "title_fr": "Quand l'ARN raconte l'infection : cartographier la bataille entre plante et microbe", "hypothesis_in_brief": "Cette hypothèse propose d'utiliser des techniques qui lisent l'ARN à l'échelle de la cellule unique et en conservant la position dans le tissu pour détecter simultanément les gènes de la plante et ceux du microbe qui l'infecte. L'idée est de vérifier si l'on peut attribuer les transcrits microbiens aux bonnes cellules végétales avec un taux d'erreur inférieur à 5 %, et si les gènes du microbe s'expriment de manière structurée dans l'espace, par exemple dans les zones colonisées.", "why_it_matters": "Comprendre comment un agent pathogène envahit une plante à l'échelle de chaque cellule pourrait aider à identifier les cellules les plus vulnérables et les gènes microbiens qui orchestrent l'infection. Cela pourrait à terme fournir des cibles pour sélectionner des variétés plus résistantes ou pour évaluer l'effet de traitements. La méthode, si elle tient ses promesses, donnerait un outil reproductible pour étudier d'autres couples plante-microbe, avec des critères quantitatifs précis.", "imagine_that": "Imaginez une ville (la plante) envahie par des cambrioleurs (les microbes). Vous voulez savoir non seulement combien il y en a, mais aussi dans quels immeubles ils se cachent et ce qu'ils y fabriquent. La technique proposée, c'est comme installer des micro-caméras dans chaque pièce : on capture des indices (les ARN) à la fois sur les habitants et sur les intrus, puis on utilise un logiciel pour démêler les indices de chacun et reconstituer la carte des cachettes. L'astuce consiste à éliminer les indices laissés dans la rue (ARN ambiant) pour ne garder que ceux réellement présents à l'intérieur des pièces.", "concretely": { "intro": "L'approche est testée en trois étapes, de la simulation informatique à l'expérience en laboratoire, avec des critères de réussite ou d'échec clairs à chaque phase.", "phase1": "D'abord, des jeux de données publics déjà existants sont réanalysés pour voir si les transcrits microbiens sont détectables à un niveau suffisant (au moins 0,1 % des lectures) et si les outils de décontamination retirent bien l'ARN ambiant sans effacer le signal intracellulaire.", "phase2": "Ensuite, une expérience minimale est menée sur un couple plante-pathogène modèle (par exemple Arabidopsis et une bactérie), avec des contrôles : une souche mutante incapable de se multiplier et des échantillons tués par la chaleur, pour calibrer le taux de fausses attributions.", "phase3": "Enfin, le protocole complet est déployé sur plusieurs réplicats biologiques et plusieurs temps d'infection, en combinant séquençage de cellules uniques et transcriptomique spatiale, pour vérifier les quatre prédictions : corrélation entre densité microbienne et activation immunitaire, enrichissement des effecteurs dans les zones colonisées, taux de faux positifs inférieur à 5 %, et structure spatiale des transcrits d'effecteurs." }, "reviewers_say": "Le panel salue la rigueur inhabituelle de la formalisation, avec des seuils quantitatifs et des contrôles mutants qui rendent l'hypothèse falsifiable. Cependant, il pointe un point faible majeur : la décontamination de l'ARN ambiant, qui est spatialement corrélé à la densité microbienne, n'est pas résolue et pourrait rendre le taux de faux positifs de 5 % inatteignable. De plus, des travaux antérieurs (Nobori et al. 2023, Tang et al. 2023) ont déjà démontré la faisabilité de approaches dual-species, ce qui réduit la nouveauté à la cartographie des effecteurs in situ. Le verdict global est de publier le brief comme une étude de preuve de concept à fort risque, mais il faudrait démontrer expérimentalement la spécificité de la décontamination, par exemple par FISH ou marquage métabolique, avant de valider les prédictions." }, "vulgarization_en": { "title": "When RNA narrates infection: mapping the battle between plant and microbe", "hypothesis_in_brief": "This hypothesis proposes the use of techniques that read RNA at the single-cell scale while preserving position within the tissue, in order to detect simultaneously the genes of the plant and those of the microbe infecting it. The aim is to verify whether microbial transcripts can be assigned to the correct plant cells with an error rate below 5%, and whether the genes of the microbe are expressed in a spatially structured manner, for example in colonised zones.", "why_it_matters": "Understanding how a pathogen invades a plant at the scale of individual cells could help identify the most vulnerable cells and the microbial genes that orchestrate infection. This could ultimately provide targets for selecting more resistant varieties or for assessing the effect of treatments. The method, if it fulfils its promise, would yield a reproducible tool for studying other plant-microbe pairs, with precise quantitative criteria.", "imagine_that": "Imagine a city (the plant) overrun by burglars (the microbes). You want to know not only how many there are, but also in which buildings they are hiding and what they are making there. The proposed technique is like installing micro-cameras in every room: you capture clues (the RNAs) from both the inhabitants and the intruders, then use software to disentangle each party's clues and reconstruct the map of the hiding places. The trick is to eliminate the clues left in the street (ambient RNA) so that only those actually present inside the rooms remain.", "reviewers_say": "The panel commends the unusual rigour of the formalisation, with quantitative thresholds and mutant controls that render the hypothesis falsifiable. However, it identifies a major weakness: the decontamination of ambient RNA, which is spatially correlated with microbial density, is not resolved and could render the 5% false-positive rate unattainable. Furthermore, prior work (Nobori et al. 2023, Tang et al. 2023) has already demonstrated the feasibility of dual-species approaches, which reduces the novelty to the mapping of effectors in situ. The overall verdict is to publish the brief as a high-risk proof-of-concept study, but the specificity of the decontamination would need to be demonstrated experimentally, for example by FISH or metabolic labelling, before the predictions are validated.", "concretely": { "intro": "The approach is tested in three stages, from computer simulation to laboratory experiment, with clear success or failure criteria at each phase.", "phase1": "First, existing public datasets are reanalysed to determine whether microbial transcripts are detectable at a sufficient level (at least 0.1% of reads) and whether decontamination tools effectively remove ambient RNA without erasing the intracellular signal.", "phase2": "A minimal experiment is then conducted on a model plant-pathogen pair (for example Arabidopsis and a bacterium), with controls: a mutant strain incapable of multiplying and heat-killed samples, to calibrate the false attribution rate.", "phase3": "Finally, the complete protocol is deployed across multiple biological replicates and multiple infection time points, combining single-cell sequencing and spatial transcriptomics, to test the four predictions: correlation between microbial density and immune activation, enrichment of effectors in colonised zones, false positive rate below 5%, and spatial structure of effector transcripts." } }, "panel_en": { "reviews": [ { "reviewer_persona": "methodologist", "overall_score": 8.2, "verdict": "accept", "confidence": 0.85, "strengths": [ "The protocol is structured in three phases with explicit GO/NO-GO criteria and quantitative thresholds, which permits an objective and progressive assessment of feasibility before any substantial financial commitment.", "The in silico validation (Phase 1) uses public datasets and a Monte-Carlo simulator to estimate statistical power under zero-inflation and variable depth, which is exemplary practice for testing the robustness of predictions before the laboratory.", "The experimental controls include a non-replicative mutant (ΔhrcC) and a heat-killed treatment, together with calibrated spike-ins, which permits intracellular microbial transcripts to be distinguished from ambient transcripts and the false positive rate to be estimated.", "The analysis plan provides for correction of collinearity with sequencing depth (regression on residuals) and the use of spatial permutations for Moran's I, which strengthens the internal validity of spatial inferences.", "Statistical power is estimated a priori by simulation for each primary prediction, with power thresholds ≥0.8 and confidence bounds, which is rare and merits emphasis." ], "weaknesses": [ "Statistical power for prediction 3 (FPR≤5%) rests on an exact binomial estimation, but the protocol does not detail the number of pseudo-replicates or permutations required to achieve sufficient precision (for example, for an FPR of 5% with an upper bound of 8%, a high number of independent observations is required, which is not specified).", "The risk of selection bias for effector genes is substantial: candidates are defined as having ≥10 UMIs in colonised clusters, which may exclude effectors that are lowly expressed but biologically relevant, and the protocol proposes no control for this selection bias.", "Phase 2 uses a single plant genotype and a single pathogen, which limits the generality of the results and may introduce confirmation bias if the team is already familiar with this system; no sensitivity analysis to other systems is planned before Phase 3.", "The protocol does not explicitly discuss potential publication bias: negative results (for example, absence of spatial structure) are not clearly intended for publication, which could encourage selective analyses.", "The measurement of microbial effector expression by scRNA-seq and spatial transcriptomics may be confounded by the presence of non-infecting extracellular microbial RNA; the protocol attempts to control for this with the ΔhrcC mutant and heat killing, but does not quantify the relative contribution of ambient versus intracellular RNA within spots, which leaves uncertainty regarding the validity of the measurement." ], "critical_questions": [ "How does the protocol ensure that the number of biological replicates (3-8) and the number of spots (≥500) are sufficient to achieve a statistical power of 0.8 for prediction 1 (Spearman rho≥0.6) under a zero-inflation of π0=0.3 and an inter-replicate CV of 0.25? Is a more detailed power analysis with variance scenarios planned?", "Which additional controls are planned to distinguish intracellular microbial transcripts from ambient transcripts adsorbed to the cell surface, beyond the ΔhrcC mutant and heat killing? For example, an RNase treatment before lysis or a comparison with secretion-deficient strains?", "How does the protocol handle the selection bias of candidate effectors (≥10 UMIs)? Is a sensitivity analysis with lower thresholds or a Bayesian approach for low-expression genes being considered?", "Does the pre-registered analysis plan (OSF) explicitly include the exclusion criteria for low-quality spots or cells, and how could these exclusions affect the false positive rate and statistical power?" ], "recommendation": "The protocol is broadly rigorous, with a stepwise approach and well-conceived controls. Acceptance is recommended on condition that the authors strengthen the justification of statistical power for prediction 3 (FPR) by specifying the number of independent observations and adding a sensitivity analysis for selection bias among effectors. It would also be useful to discuss publication bias explicitly and to specify the exclusion criteria for low-quality data in the pre-registration." }, { "reviewer_persona": "domain_expert", "overall_score": 5.8, "verdict": "weak_accept", "confidence": 0.78, "strengths": [ "The causal chain is generally well structured and incorporates explicit quantitative controls (calibrated spike-in, false positive rate ≤5%, Moran's I threshold ≥0.3, Pearson residual r <0.1), which exceeds most dual-species proposals that remain qualitative. This formalisation into falsifiable criteria is a genuine methodological strength.", "The distinction between computational decontamination (SoupX/DecontX) and validation by non-replicating mutants is pertinent: it directly addresses the problem of ambient microbial RNA, which is the most documented failure point of dual-species approaches in infected tissue.", "The identification of step 4 (spatial normalisation of the collinearity between microbial density and sequencing depth) as a statistical bottleneck is a subtle point that is rarely made explicit in the ST literature applied to pathosystems; this demonstrates a genuine understanding of the artefacts specific to spatial data." ], "weaknesses": [ "The positioning relative to the state of the art is the major weakness: Nobori et al. (2023, DOI:10.1101/2023.04.10.536170) already demonstrate scRNA-seq + time-resolved ST on Arabidopsis infected with Pseudomonas syringae, with mapping of pathogen distribution, and Tang et al. (2023, DOI:10.1101/2023.04.03.535386) already assign cells interacting with hyphae. The proposal presents itself as a methodological transfer whereas the proof of concept already exists; the residual novelty (effector mapping in situ at cellular resolution) is neither isolated nor defended against these works.", "The hypothesis of a microbial capture efficiency ≥0.1% of total reads is plausible for intracellular bacteria at high load, but highly fragile for filamentous fungi or effectors expressed transiently at low level; no power analysis is provided linking microbial load, MOI, sequencing depth and detectability of a given effector. The 0.1% threshold appears to be a figure posited a priori rather than derived.", "The removal of ≥90% of ambient RNA by DecontX/SoupX without eliminating genuinely intracellular transcripts is an unsupported claim: these methods rely on ambient reference profiles and fail precisely when the biological signal is spatially correlated with contamination (the typical case of colonised niches). The mechanism does not propose an orthogonal control (e.g. FISH on a candidate effector) to adjudicate.", "The assignment of microbial reads to cell clusters by co-occurrence within droplets (step 3) conflates co-encapsulation with actual infection; the control using non-replicating mutants validates biological specificity but not assignment resolution. No estimate of the host–microbe doublet rate nor of transcript leakage between adjacent cells is proposed.", "The bibliographic base is correct but asymmetric: it cites abundantly the recent plant–pathogen scRNA-seq/ST atlases and omits the critical methodological literature on single-cell decontamination (notably the work showing the limitations of SoupX/DecontX on low-load data), as well as the fungal dual-species RNA-seq work (e.g. Dual RNA-seq of foliar pathosystems) that sets the ceiling of effector detectability." ], "critical_questions": [ "Which power calculation links the expected microbial load (CFU or fungal biomass per cell), the MOI, the sequencing depth per cell/spot and the probability of detecting a lowly expressed effector transcript (e.g. <10 copies per infected cell)? Without this calculation, the 0.1% threshold is not justified.", "How does the mechanism distinguish a genuinely intracellular effector transcript from a transcript adsorbed to the cell surface or released into the apoplast, given that DecontX/SoupX model the ambient in a global rather than compartmentalised manner? Is an orthogonal control (RNAscope on 2–3 predicted effectors) planned?", "Does validation by non-replicative mutants truly control the assignment, or only the absence of biological signal? How is the host–microbe doublet rate and the leakage of transcripts between adjacent spots measured in spatial barcoding?", "Is the Moran's I ≥0.3 threshold calibrated on simulations of zero-inflated data with the sequencing depth observed in this type of experiment, or is it borrowed from the ST literature on uninfected tissues? Is the null distribution of Moran's I under spatial zero-inflation established for this design?", "What is the operational difference between this proposal and Nobori et al. 2023, beyond the addition of effector mapping? If effector mapping is the true delta, why not formulate it as the central hypothesis rather than as step 5 of an already demonstrated chain?" ], "recommendation": "A weak_accept is recommended: the proposal is methodologically sound and formalises useful falsifiable criteria, but its positioning relative to Nobori et al. 2023 and Tang et al. 2023 must be rewritten to isolate the actual delta (spatial mapping of microbial effectors at cellular resolution) and to defend it explicitly. A power analysis linking microbial load, depth and effector detectability must imperatively be added, together with an orthogonal decontamination control (FISH/RNAscope on candidate effectors) to render the hypothesis testable beyond SoupX/DecontX. As it stands, the causal chain is plausible but the novelty is overestimated and the bottleneck of spatially correlated decontamination is not resolved." }, { "reviewer_persona": "contrarian", "overall_score": 3.5, "verdict": "reject", "confidence": 0.82, "strengths": [ "The experimental architecture is ambitious and incorporates calibration controls (spike-in, non-replicative mutant, permutation of ambient transcripts) that demonstrate a genuine awareness of the cross-contamination problem, which is often overlooked in the dual-species literature.", "The formalisation of the null hypotheses and effect bounds (Moran's I ≥0.3, FPR ≤5%, log2 enrichment ≤-1) is unusually rigorous and renders the hypothesis falsifiable, which is commendable." ], "weaknesses": [ "FAIL REASON #1: The Step 1 link is the most probable point of failure: the isolation of protoplasts or nuclei from infected tissues selectively destroys or lyses microbial cells and intracellular structures (haustoria, arbuscules), so that the fraction of genuinely intracellular microbial transcripts captured will be far below the claimed 0.1% threshold; the microbial transcripts detected will be predominantly ambient or extracellular, and SoupX/DecontX decontamination cannot distinguish an authentic intracellular transcript from a co-captured ambient transcript — the hypothesis of ≥90% decontamination without loss of intracellular transcripts is untested and probably false, since both populations share the same compositional signature.", "FAIL REASON #2: The Step 3 causal chain (assignment of microbial reads to plant cell clusters by co-occurrence within droplets) rests on an unvalidated colocalisation hypothesis: in a droplet containing a plant cell and several microbial genomes, co-occurrence does not prove intracellular infection but only proximity; the non-replicative mutant controls replication but not residual extracellular adhesion, and the calibrated MOI spike-in cannot distinguish a true assignment from surface contamination — the 5% false-positive rate is therefore structurally unattainable, and the permutation test of ambient labels does not model the spatial correlation between microbial density and sequencing depth, which guarantees a false positive.", "FAIL REASON #3: Prediction 1 (Spearman rho ≥0.6 between microbial density and immune activation score after normalisation) is an expected artefact: microbial density and sequencing depth are collinear by construction (more microbes = more total reads = deeper spots), and regression on depth residuals does not remove this collinearity if depth is itself a causal mediator of density; moreover, with π0=0.3 zero-inflation and an inter-replicate CV of 0.25, the stated power of 0.8 is optimistic and the Moran's I ≥0.3 on depth residuals is mechanically biased towards zero or towards a neighbourhood artefact, rendering the spatial structure uninterpretable as biological." ], "critical_questions": [ "How does the protocol distinguish, at the droplet level, an authentic intracellular microbial transcript from an ambient transcript adsorbed to the plant cell wall surface, given that SoupX/DecontX both estimate from the same count matrix and have no intracellular marker?", "What is the actual statistical power of the Moran's I ≥0.3 test on depth residuals when microbial density already explains the majority of depth variance, and how does the manuscript demonstrate that the residual signal is not an artefact of spatial smoothing (autocorrelation induced by the normalisation itself)?", "Does the non-replicative mutant genuinely control for false-positive assignment, or does it merely shift the problem to non-replicative extracellular adhesion, which remains co-captured and indistinguishable from intracellular infection?" ], "recommendation": "Before any claim of FPR ≤5 %, the fraction of microbial transcripts that are genuinely intracellular within the droplets must be demonstrated experimentally, by FISH or by metabolic labelling (BONCAT/click-chemistry) coupled to capture; without this independent validation, Steps 1 and 3 remain untested hypotheses and predictions 1, 3 and 4 are artefacts of depth and co-occurrence. The reviewer recommends restricting the hypothesis to a model system in which intracellular infection is verifiable by correlative microscopy, and reporting explicitly the rate of ambient microbial transcripts measured on supplemented uninfected tissue, rather than postulating a 90 % decontamination." }, { "reviewer_persona": "industrialist", "overall_score": 6.5, "verdict": "weak_accept", "confidence": 0.65, "strengths": [ "The addressable market is real: crop-protection companies (Bayer Crop Science, Syngenta, Corteva) and seed companies (Limagrain, KWS) are investing heavily in genetic resistance and biostimulants. A dual-species single-cell/spatial tool would enable the screening of genotypes and microbial effectors at unprecedented resolution, justifying a high licence fee (estimated TAM of €150–300M for molecular phenotyping services in plant pathology, CAGR ~12%).", "The competitive advantage is twofold: (1) the IP barrier can be built on the calibrated decontamination pipelines and spike-in controls, which are difficult to replicate without the requisite know-how; (2) the integration of niche colonisation theory and ETI provides a proprietary interpretive framework that competitors (10x Genomics, NanoString) do not offer natively for plant–microbe interactions.", "The three-phase protocol with an in silico kill-switch (Phase 1 at €0–2k) is a major commercial asset: it enables feasibility to be validated before any substantial commitment, reducing investment risk and facilitating funding through grants (Horizon Europe, BBSRC) or industrial partnerships." ], "weaknesses": [ "Market size is constrained by the fragmentation of pathosystems: each plant–pathogen pair requires its own calibration (spike-in, decontamination, scRNA-seq baseline). Development costs per new host (€60–120k per pathosystem) make the business model difficult to scale without extensive automation, which delays ROI.", "Existing competition is underestimated: spatial transcriptomics platforms (10x Visium, Curio Seeker) and decontamination tools (SoupX, DecontX) are open-source or low-cost. The added value lies in integration, but potential clients (academic and industrial) may internalise these pipelines, reducing willingness to pay for a third-party service.", "The commercialisation timeline is realistic but long: current TRL ~3–4 (validation under controlled conditions); progression to a commercial product (kit or service) requires 24–36 months after Phase 3, with additional regulatory and validation costs (ISO standards for sequencing services, for example). The risk of failure in Phase 3 (prediction 3 at FPR≤5%) remains high and could invalidate the entire value proposition." ], "critical_questions": [ "What is the precise revenue model: sale of calibrated spike-in kits, licensing of the software pipeline, or a turnkey profiling service? Are the unit price per sample and the annual volume required to reach profitability compatible with the fragmentation of the market?", "How is intellectual property protected on the spike-in controls and the decontamination thresholds, given that the underlying algorithms (SoupX, DecontX) are open-source and that competitors can develop variants? Is a patent on the dual-species application genuinely defensible?", "What is the commercial validation plan with an industrial partner (for example, a seed company or a manufacturer of plant protection products) before Phase 3, in order to secure a first contract and reduce market risk?" ], "recommendation": "Phase 1 in silico is to be launched immediately (cost <€2k) to confirm technical feasibility and identify the most promising pathosystems (e.g. Arabidopsis-Pseudomonas, rice-Magnaporthe). In parallel, an industrial partner (Limagrain, Bayer) is to be approached to co-finance Phase 2 and secure a first commercial use case, while a patent is filed on the calibrated decontamination pipeline and the spike-in controls before any publication. Phase 3 is not to be initiated without an in-principle commitment from a paying client." }, { "reviewer_persona": "funding_strategist", "overall_score": 7.5, "verdict": "weak_accept", "confidence": 0.8, "funding_programs": [ { "program": "ANR Appel à projets générique 2026 – PRC (Projet de Recherche Collaborative)", "agency": "Agence Nationale de la Recherche", "fit_score": 0.85, "typical_budget": "300–600 k€ (volet PRC)", "success_rate": "≈20–25% (PRC)", "next_deadline": "Octobre 2025 (lettre d'intention) / Mars 2026 (dossier complet)", "rationale": "L'ANR PRC finance des projets de recherche fondamentale à consortium restreint (2–4 partenaires) sur 36–48 mois, avec un budget compatible avec l'extension du protocole à plusieurs pathosystèmes. La structure en phases GO/NO-GO et les critères quantitatifs de falsifiabilité correspondent aux attentes de l'ANR en matière de rigueur méthodologique et de gestion des risques. Le volet « Tremplin » de l'ANR pourrait également être visé pour la phase 1–2, avant de basculer vers un PRC." }, { "program": "Horizon Europe – HORIZON-CL6-2026-02-BIODIV-01 (ou appel équivalent sur la santé des plantes et les interactions hôte-microbe)", "agency": "Commission Européenne (DG Recherche et Innovation)", "fit_score": 0.7, "typical_budget": "3–5 M€ (projet collaboratif)", "success_rate": "≈15–20% (appels CL6)", "next_deadline": "Février 2026 (appel à propositions)", "rationale": "Les appels du cluster 6 (Alimentation, Bioéconomie, Ressources naturelles, Agriculture et Environnement) financent des projets collaboratifs sur les interactions plante-microbe et la santé des cultures. L'hypothèse s'inscrit dans le thème « outils moléculaires pour la surveillance des pathogènes » et pourrait être intégrée comme work package méthodologique dans un consortium plus large incluant des partenaires européens experts en transcriptomique spatiale et en épidémiologie végétale. Le TRL bas est compensé par le caractère fondamental de l'appel, mais la taille du consortium requise (≥3 pays) nécessite un partenaire coordinateur expérimenté." }, { "program": "ERC Starting Grant 2026 (ou Consolidator Grant 2026)", "agency": "European Research Council", "fit_score": 0.65, "typical_budget": "1,5–2 M€ (Starting) / 2–2,5 M€ (Consolidator)", "success_rate": "≈10–12% (Starting)", "next_deadline": "Octobre 2025 (Starting) / Février 2026 (Consolidator)", "rationale": "L'ERC finance des projets pionniers à haut risque et haut gain portés par un chercheur principal, sans consortium obligatoire. L'hypothèse, par son caractère méthodologique ambitieux (cartographie dual-species à l'échelle de la cellule unique) et sa falsifiabilité, correspond à l'esprit « frontier research » de l'ERC. Cependant, le TRL très bas et l'absence de données préliminaires solides constituent un frein ; une demande ERC serait plus compétitive après obtention des résultats de phase 2 (preuve de concept expérimentale) et avec un CV de PI démontrant une expertise reconnue en transcriptomique spatiale et en interactions plante-microbe." } ], "strengths": [ "The three-phase protocol with quantitative GO/NO-GO criteria and a very low initial investment (≤€2k in the in silico phase) constitutes a major asset for funders: it demonstrates rigorous risk management and allows false leads to be eliminated rapidly before any substantial commitment.", "The hypothesis combines scRNA-seq and spatial transcriptomics on infected tissues, with falsifiable predictions (FPR≤5%, Moran's I≥0.3, rho≥0.6) and mutant controls (ΔhrcC), which meets the growing requirements for reproducibility and statistical rigour of Horizon Europe and ANR calls.", "The total budget (€25–120k) and duration (10–18 months) are perfectly calibrated for a \"springboard\" or \"proof-of-concept\" call, with a strong leverage effect: the Phase 2 results can serve as the basis for an ERC application or a larger collaborative project." ], "weaknesses": [ "The current TRL is very low (TRL 2–3): this constitutes a methodological validation, not yet a translational application, which excludes innovation- or technology-maturation-oriented calls and limits eligibility to fundamental research programmes.", "The consortium is not defined and the hypothesis rests on a single plant–pathogen system (unspecified); without a partner with expertise in spatial transcriptomics and computational analysis of dual-species data, the credibility of the data management plan and of reproducibility is weakened.", "The expected microbial load (≥0.1% of UMIs) is close to the detection limits of current platforms, and decontamination (SoupX/DecontX) remains an unresolved methodological bottleneck at scale; the risk of failure in Phase 2 is real and must be explicitly mitigated in the proposal." ], "critical_questions": [ "Which precise plant–pathogen system is targeted, and are public or preliminary data available demonstrating that the intracellular microbial load reaches the threshold of 0.1% of UMIs in that system?", "Which partners or collaborations are envisaged for the spatial transcriptomics component (10x Visium) and for the dual-species bioinformatics analysis, and how does the consortium guarantee access to the platforms and to expertise in decontamination?", "How will the data management plan and the sharing of dual-species datasets be structured to meet FAIR requirements and the open-science policies of the funders (Horizon Europe, ANR)?" ], "recommendation": "Position this hypothesis as a high-risk, high-gain methodological proof of concept, in response to an ANR \"Tremplin\" or \"PRC\" call with a restricted consortium (1–2 partners) including a spatial transcriptomics platform. Use the Phase 1 in silico results as preliminary evidence in the letter of intent, and plan for an ERC Starting Grant or Consolidator application in Phase 3 to extend the framework to several pathosystems. The narrative should emphasise falsifiability, risk management and data openness, rather than premature applicative promises." } ], "meta_review": { "consensus_score": 6.3, "verdict": "publish_brief", "brief_quality_gate": true, "llm_verdict": "publish_brief", "llm_consensus_score": 6.4, "key_consensus": [ "All reviewers acknowledge the unusual rigour of the formalisation: quantitative GO/NO-GO criteria, falsifiable thresholds (FPR≤5%, Moran's I≥0.3, rho≥0.6), mutant controls (ΔhrcC) and calibrated spike-ins. The methodologist, the domain_expert and even the contrarian welcome this falsifiability as a genuine methodological strength.", "The central bottleneck identified convergently is the decontamination of ambient microbial RNA: the methodologist, the domain_expert and the contrarian agree that SoupX/DecontX do not reliably distinguish an authentic intracellular transcript from a co-captured ambient transcript, and that an orthogonal control (FISH/RNAscope, BONCAT) is required.", "The three-phase structure with a very low initial investment (≤2 k€) is unanimously perceived as a strength: the methodologist, the industrialist and the funding_strategist regard it as exemplary risk management enabling false leads to be ruled out before heavy commitment.", "The causal chain is judged plausible and well structured by the entire panel, including the sceptics; the disagreement concerns the novelty and robustness of the links, not the logical coherence of the chain." ], "key_disagreements": [ "The positioning relative to the state of the art is the principal point of divergence: the domain_expert judges that Nobori et al. (2023) and Tang et al. (2023) have already demonstrated the proof of concept for dual-species scRNA-seq+ST on infected tissues, reducing the residual novelty to the in situ effector mapping, which is neither isolated nor defended. The methodologist and the funding_strategist do not raise this prior art and treat the proposal as methodologically novel.", "The feasibility of the FPR≤5% threshold is contested head-on: the contrarian (confidence 0.82) maintains that Steps 1 and 3 render this threshold structurally unattainable (selective lysis of microbial cells, confounding of co-occurrence with infection), whereas the methodologist judges the protocol capable of reaching this threshold provided that clarifications are given on the number of independent observations.", "The microbial load threshold of ≥0.1% of UMIs is judged plausible by the methodologist but \"posited a priori rather than derived\" by the domain_expert, who requires a power analysis linking microbial load, MOI, depth and detectability of low-expression effectors.", "Prediction 1 (Spearman rho≥0.6) is regarded as an artefact of depth/density collinearity by the contrarian, whereas the methodologist validates it subject to a correction for collinearity by regression on residuals and spatial permutations." ], "revision_guidance": [], "critical_path": "The most decisive factor is the experimental demonstration, by an independent orthogonal control (FISH/RNAscope on candidate effectors, or BONCAT metabolic labelling coupled to capture), of the fraction of microbial transcripts that are genuinely intracellular and captured within the droplets/spots. Without this validation, Steps 1 and 3 remain untested hypotheses and predictions 1, 3 and 4 may be artefacts of depth and co-occurrence — this is the convergent breaking point between the domain_expert and the contrarian, and the sine qua non condition for the FPR≤5% threshold to be credible.", "final_recommendation": "The panel recognises a methodologically rigorous, falsifiable proposal with well-calibrated risk, but whose novelty is overstated relative to Nobori et al. (2023) and Tang et al. (2023), and whose critical link — the decontamination of spatially correlated ambient RNA — remains unresolved. The brief is published as-is at iteration 2, but it must be read as a high-risk proof of concept: the absolute priority is to add an orthogonal decontamination control and a power analysis linking microbial load, depth and detectability of effectors. The actual delta (spatial mapping of effectors at cellular resolution) must be reframed as the central hypothesis rather than as step 5 of an already demonstrated chain. The contrarian's concerns (FAIL REASON #1 and #3) are documented and must be addressed explicitly in the experimental plan before any commitment to Phase 3." } } }