Figures (2)  Tables (2)

    • Figure 1. 

      A strategic roadmap for integrating single-cell and spatial multi-omics to decode plant physiological complexity. This schematic illustrates the workflow for constructing a multi-dimensional 'digital twin' of plant tissues. (a) Single-cell and single-nucleus transcriptomic profiling resolves cellular heterogeneity and developmental trajectories. (b) Spatially resolved transcriptomic, metabolomic, and proteomic methods map molecular states back to tissue coordinates. The capture strategy shown in panel (b) represents one illustrative technical route for spatial transcriptomics and does not encompass all available spatial transcriptomics platforms. (c) Cross-modality integration generates a spatially anchored molecular atlas of plant tissues. (d) Plant-specific constraints—including rigid cell walls, vacuolation, and analyte delocalization—shape experimental design and interpretation. Created with BioRender.

    • Figure 2. 

      Minimum operational framework for a plant spatial 'digital twin'. The framework includes spatially registered histology, a matched sc/snRNA-seq reference for improved detection of rare cell populations and spatial deconvolution, same-section co-detection of complementary molecular layers, region-matched proteomics from adjacent sections, explicit cross-section registration with uncertainty control, and orthogonal validation of inferred relationships. Created with BioRender.

    • Modality class Representative platforms/methods Typical output/use Main plant constraints Failure modes/QC Status in plants
      Protoplast-based scRNA-seq 10x Chromium; Drop-seq-type workflows; DNBelab C Series; Smart-seq2/3 Single-cell transcriptomes; atlases, trajectories, stress states Protoplasting bias; stress artifacts; selective cell loss Low viability; composition bias; organellar RNA; QC: viability, genes/UMIs, doublets Mature, but tissue-dependent
      snRNA-seq 10x Chromium nuclei workflows; DNBelab C Series; plate- or droplet-based nuclei workflows Nuclear transcriptomes; difficult, lignified, mature, or frozen tissues Nuclear/cytoplasmic differences; debris, starch, metabolites Ambient RNA; low complexity; QC: nuclei integrity, genes/UMIs, marker concordance Highly practical
      Single-cell chromatin/
      multiome      		 10x Single Cell ATAC; 10x Multiome; sci-ATAC-seq-type workflows Accessibility ± RNA; regulatory inference High-quality nuclei required; sparse signal Low fragments; poor TSS; QC: FRiP/TSS, doublets, integration quality Emerging to moderately established
      Capture-based whole-transcriptome ST 10x Visium/Visium HD; Stereo-seq; DBiT-seq-type workflows; plant-adapted commercial ST services Section-wide spatial transcriptomes; zonation and gradients Section quality; permeabilization; cell walls; vacuolation; cuticles Diffusion; mixed capture; organellar RNA; QC: section integrity, genes/UMIs, spatial concordance Most mature spatial transcriptomic class
      Imaging-/ hybridization-based targeted ST smFISH; RNAscope; MERFISH; seqFISH-type methods; in situ sequencing-related workflows High-precision localization of selected transcripts Probe penetration; autofluorescence; optical opacity Background; undercounting; QC: signal/background, specificity, reproducibility Powerful, but not routine
      ROI-/LCM-guided spatial transcriptomics LCM-RNA-seq; Geo-seq-type workflows; histology-guided ROI profiling Region-specific transcriptomes; defined anatomical domains RNA preservation; low input; regional contamination Degradation; low yield; QC: RNA quality, region purity, library complexity Practical for focused questions
      Spatial proteomics: imaging-based Multiplex IF; cyclic IF; CODEX-type workflows; imaging mass cytometry (IMC) In situ protein localization and tissue-domain validation Limited antibodies; epitope preservation; autofluorescence Weak specificity; background; QC: antibody validation, controls, image quality Emerging and reagent-limited
      Spatial proteomics:
      LCM-guided      		 LCM-LC–MS/MS; low-input microproteomics; nanoPOTS-type workflows Region-matched proteomes Low input; extraction from wall-rich tissues; peptide loss Shallow depth; variable recovery; QC: peptide/protein counts, reproducibility Promising, but demanding
      Spatial metabolomics/
      MSI      		 MALDI-MSI; DESI-MSI; SIMS/TOF-SIMS; AFADESI-MSI Spatial metabolite maps; defense and stress chemistry Delocalization; ion suppression; wax/cuticle barriers Analyte loss; annotation uncertainty; QC: freezing, matrix consistency, calibration, validation Increasingly important, but method-sensitive

      Table 1. 

      Practical comparison of major single-cell, single-nucleus, and spatial omics modalities in plant research.

    • Analytical goal/research scenarioPreferred omics layer(s)Recommended modality/representative methodsKey sample-preparation prioritiesCore QC checkpointsMajor pitfalls/interpretation caveats
      Cell-type and cell-state discovery in dissociable tissuesTranscriptomeProtoplast-based scRNA-seq; 10x Chromium; Drop-seq-type workflows; Smart-seq2/3Optimize enzymatic digestion, osmotic balance, and handling time; minimize protoplasting stress; process biological replicates independentlyCell viability; genes/UMIs per cell; organellar RNA fraction; doublets; recovery of expected marker-defined populationsDissociation bias can distort cell composition; stress-induced transcription may masquerade as biology; fragile or highly vacuolated cells may be underrepresented
      Profiling difficult, mature, lignified, frozen, or chemically interfering tissuesTranscriptomesnRNA-seq; nuclei-compatible droplet workflows; plate-based nuclear RNA-seqPreserve nuclei integrity; reduce debris, starch, mucilage, and phenolic carryover; standardize homogenization and filtrationNuclei integrity; genes/UMIs per nucleus; ambient RNA; organellar contamination; agreement with expected markersNuclear and whole-cell transcriptomes are not directly equivalent; low-complexity nuclei and background contamination can inflate false heterogeneity
      Organ-wide spatial mapping of tissue domains, zonation, and expression gradientsSpatial transcriptomeCapture-based whole-transcriptome ST; 10x Visium/Visium HD; Stereo-seq; DBiT-seq-type methodsOptimize sectioning, fixation, permeabilization, RNA capture, and histology registration for each tissue typeSection integrity; genes/UMIs per spot/bin; organellar fraction; histology concordance; spatial reproducibility across sectionsSpot/bin signals often reflect mixed-cell capture rather than true single-cell resolution; diffusion and registration errors can distort boundaries
      High-resolution validation of selected spatial markersTargeted spatial transcriptomesmFISH; RNAscope; MERFISH; seqFISH-type methodsOptimize fixation, probe penetration, autofluorescence suppression, and imaging depth; preserve anatomy for segmentationSignal-to-background ratio; probe specificity; reproducibility across sections; segmentation qualityLimited gene panels restrict discovery; poor penetration or high background may produce false negatives or misleading enrichment patterns
      Molecular profiling of anatomically defined regions, rare structures, or histologically selected domainsRegion-resolved transcriptome and/or proteomeLCM-RNA-seq; Geo-seq-type workflows; histology-guided ROI profiling; LCM-LC–MS/MS; low-input microproteomicsMaintain RNA/protein integrity during sectioning and microdissection; define ROIs precisely; minimize contamination from adjacent regionsRNA quality or protein recovery; region purity; library/proteome complexity; concordance with histologyRegion-level data should not be overinterpreted as true single-cell profiles; low input and contamination are common limiting factors
      Inference of regulatory programs and chromatin dynamicsChromatin accessibility, optionally paired with transcriptomescATAC-seq; 10x Single Cell ATAC; sci-ATAC-seq-type workflows; single-cell multiome; 10x MultiomeUse exceptionally clean nuclei; reduce debris and clumps; optimize accessibility workflow; prepare matched RNA references where possibleFragment counts; TSS enrichment; FRiP or equivalent; doublets; cluster stability; concordance with RNA-based annotationData sparsity is high; annotation is often indirect; low-quality nuclei can mimic biological variability; inferred regulatory links require orthogonal validation
      Protein-level spatial profiling and validation of transcript-defined domainsProtein spatial layerMultiplex IF; cyclic IF; CODEX-type workflows; imaging mass cytometry (IMC); immunostaining; LCM-guided microproteomicsValidate antibodies rigorously; optimize fixation, epitope preservation, autofluorescence reduction, and extraction from wall-rich tissuesAntibody specificity; positive/negative controls; reproducibility across sections; peptide/protein identifications for MS-based workflowsAntibody availability is a major bottleneck in plants; transcript and protein abundance may diverge substantially; low-input proteomics may have limited depth
      Mapping metabolites, specialized chemistry, defense compounds, or storage molecules in situMetabolite spatial layerMALDI-MSI; DESI-MSI; SIMS/TOF-SIMS; AFADESI-MSIRapid freezing; prevent analyte delocalization; optimize matrix deposition or ionization conditions; handle cuticle/wax-rich tissues carefullySpatial fidelity; matrix consistency where relevant; mass accuracy; calibration; reproducibility; confidence of metabolite annotationMetabolite identification is often uncertain; ion suppression, analyte delocalization, and tissue chemistry strongly affect signals; transcript levels do not necessarily predict metabolite abundance
      Cross-modal integration, comparative validation, and atlas building in non-model or crop speciesRNA + chromatin and/or protein and/or metabolitesc/snRNA-seq integrated with scATAC-seq, multiome, ST, imaging proteomics, MSI, or ROI-guided omicsHarmonize sample processing and metadata across modalities; use matched or serial sections where relevant; validate marker conservation and species-specific annotationsRegistration quality; modality concordance; mapping rate; annotation confidence; replicate stability; robustness of integrated conclusionsCross-modal integration can create false confidence if individual layers are weak; cross-species marker transfer may be misleading; apparent novelty may reflect incomplete annotation or protocol bias

      Table 2. 

      Cross-omics practical decision guide for plant single-cell, single-nucleus, and spatial profiling.