Transcriptomics
Spatial RNA maps from FASTQs or vendor outputs to tissue domains and deconvolution stats while keeping architecture dissociated scRNA-seq loses [1], with pinned Space Ranger workflows and a Methods draft.
| Category | Details |
|---|---|
| Supported platforms / instruments | Primary: 10x Genomics Visium v1/v2 (CytAssist), Visium HD, Xenium In Situ (Prime 5K). Illumina or MGI DNBSEQ-sequenced Visium FASTQs when scoped at kickoff. NanoString CosMx SMI, MERFISH, and legacy Spatial Transcriptomics arrays on request |
| Input requirements | Visium v2: ≥25,000 read pairs/tissue-covered spot, 4,992 spots per 6.5 mm capture area [2]. Visium HD (6.5 mm, 100% tissue): ≥275M read pairs (FFPE), ≥500M (Fixed Frozen), ≥700M (Fresh Frozen) [2]. Xenium: vendor cell-segmented outputs or raw bundles; FFPE or fresh frozen sections |
| Reference builds supported | Human GRCh38-2024-A (GENCODE v44 / Ensembl 110); mouse GRCm39-2024-A (GENCODE vM33 / Ensembl 110); Visium FFPE probe sets per 10x reference bundles; custom references on request |
| Primary tools (with versions) | Space Ranger 3.1.3; Scanpy 1.12.1; Squidpy 1.8.1; Seurat 5.2.1; pyBanksy 1.3.4; cell2location 0.1.5; SPOTlight 1.6.3; SpotClean 1.12.1; harmonypy 1.2.3; bin2cell (Visium HD, on request) |
| Typical turnaround time | 4-6 weeks (standard Visium cohort, 4-12 sections); 6-10 weeks (Visium HD cell reconstruction, multi-slice integration, or Xenium). Confirmed at kickoff |
| Deliverable formats | .h5ad, .rds, Space Ranger outputs, spatial coordinate files; PDF/SVG spatial plots; HTML QC report; documented R/Python scripts; Methods draft |
| Key cited best-practice reference | Moses & Pachter [1], Nature Methods; Salas et al. [3], Nature Methods; Long et al. [4], Genome Biology |
| Custom / bespoke analysis | Non-standard inputs, outputs, and methods scoped at kickoff, for example Visium HD bin2cell segmentation, Xenium segmentation QC, cross-platform integration, or client-specified spatial statistics |
Spatial transcriptomics assigns RNA counts to physical locations on a tissue section, producing a spot- or cell-level count matrix linked to (x, y) coordinates and, when available, a registered H&E image. Unlike dissociated scRNA-seq, expression is measured in situ, so cell-cell neighborhoods and tissue domains remain intact [1, 5]. On 10x Visium, 55 µm spots typically capture multiple cells, a mini-bulk readout that needs spatial statistics or deconvolution for cell-type claims [2, 6]. Pepkio starts from FASTQs, Space Ranger outputs, or preprocessed objects and returns annotated spatial datasets with QC at each step. Custom inputs, deliverable formats, and analyses beyond the standard workflow are agreed at kickoff. See the spatial transcriptomics glossary.
Spatial transcriptomics is a fit when the biological question depends on where genes are expressed, such as tumor-stroma boundaries, immune niches, or anatomical layers, not only which cell types are present.
| Approach | Best for | Limitations | Approximate cost range |
|---|---|---|---|
| Spatial transcriptomics (Visium, Xenium) | Tissue domains, cell-cell niches, spatial DE, microenvironment architecture | Spot mixing on Visium; platform-specific capture; sectioning and registration constraints | Library prep + sequencing and bioinformatics vary by platform, section count, and depth |
| scRNA-seq (droplet) | Cell-type discovery, rare populations, trajectory inference | Does not retain native spatial context; dissociation stress and ambient RNA artifacts | Lower per-cell sequencing cost than subcellular spatial; no tissue architecture |
| Bulk RNA-seq | Condition contrasts when tissue is homogeneous | Averages across cell types and locations; no spatial resolution | Lower per-sample cost than spatial for modest cohorts |
Pepkio confirms FASTQ pairing, Visium slide serial numbers, CytAssist or H&E image availability, and Xenium bundle completeness when applicable. Platform, preservation method, capture-area coverage, and covariates are recorded in sample_manifest.csv. Sub-threshold depth or incomplete coverage is flagged before downstream work [2, 10].
Tools used: fastqc / fastp as needed; Loupe Browser tissue-coverage estimates when client provides them
Output: sample_manifest.csv with library IDs, platform, read counts, capture-area coverage, and QC flags
For 10x Visium and Visium HD, Pepkio runs spaceranger count with GRCh38-2024-A or GRCm39-2024-A references [2]. Fraction reads in tissue, reads per spot, and genes per spot are compared against vendor ranges. Preprocessed Space Ranger or Xenium outputs can be imported when clients provide them.
Tools used: Space Ranger 3.1.3
Output: filtered_feature_bc_matrix/, spatial/, metrics_summary.csv, web_summary.html; Visium HD binned matrices when applicable
Count matrices and coordinates are imported into AnnData or Seurat objects with raw UMI counts in a dedicated layer and H&E or CytAssist images in the spatial slot [11]. Xenium segmented cell matrices retain segmentation QC flags.
Tools used: Scanpy 1.12.1 (read_visium) or Seurat 5.2.1 (Load10X_Spatial)
Output: Per-sample .h5ad or .rds with counts layer, spatial coordinates, and image metadata
Spots or cells with low UMI counts, low gene complexity, or high mitochondrial fractions are flagged using sample-adaptive thresholds [1, 3]. For Visium, fraction reads in tissue, sequencing saturation, and spot counts are compared to vendor recommendations [2].
Tools used: Scanpy 1.12.1 or Seurat 5.2.1
Output: Filtered object; QC plots for nCount_Spatial, nFeature_Spatial, percent.mt; sample_qc_summary.csv
Visium spots can capture transcripts from neighboring locations, a spot-swapping effect that inflates spatial correlation [12]. When raw and background counts are available, Pepkio applies SpotClean before domain calling. Visium HD and Xenium skip this step or use platform-specific QC.
Tools used: SpotClean 1.12.1
Output: Decontaminated count matrix; spot_contamination_rate in metadata; SpotClean diagnostic plots overlaid on tissue
Library-size normalization and log transformation suppress extreme count values while retaining spatial structure [4]. Pepkio selects highly variable genes for dimensionality reduction and computes spatially variable genes (SVGs) with Squidpy Moran's I, validating top SVGs on the tissue image [3, 11].
Tools used: Scanpy 1.12.1; Squidpy 1.8.1
Output: Normalized layers; highly_variable_genes.csv; svg_gene_table.csv with Moran's I scores and FDR
A spatial neighborhood graph connects each spot or cell to its physical neighbors [11]. Pepkio identifies tissue domains with BANKSY, which combines expression and neighborhood context, or Leiden clustering on spatial embeddings, mapping domain labels onto H&E for biological review [4, 13].
Tools used: Squidpy 1.8.1; pyBanksy 1.3.4; leidenalg 0.10.2
Output: domain_assignments.csv; spatial domain maps on H&E; neighborhood graph object
Visium spots contain multiple cell types; deconvolution estimates per-spot abundance from a matched scRNA-seq reference [6]. Pepkio trains cell2location or SPOTlight signatures and validates with marker genes [14, 15]. Reference-free deconvolution is scoped when no atlas exists.
Tools used: cell2location 0.1.5 or SPOTlight 1.6.3
Output: deconvolution_proportions.csv (spot × cell type); spatial abundance maps; reference signature diagnostics
Multi-patient or multi-slide projects are integrated with Harmony when shared spatial domains are present [16]. Cross-slide 3D alignment or Visium-Xenium integration is scoped separately when required [4].
Tools used: harmonypy 1.2.3 or Seurat 5.2.1 integration workflows
Output: Harmonized embedding; before/after spatial plots by batch and condition; integration QC metrics
Domain-wise or condition-wise DE uses pseudobulk limma-voom with ≥3 replicates per group; spot-level Wilcoxon tests with Benjamini-Hochberg FDR when pseudobulk is not feasible [1]. Results export as ranked gene lists with log₂ fold-change and adjusted p-values.
Tools used: limma 3.62.2 / edgeR 4.4.2 (R) or Scanpy rank_genes_groups (Python)
Output: deg_by_domain.csv; spatial feature plots; final .h5ad/.rds; scripts; Methods draft
For 10x Visium CytAssist v2, Pepkio recommends ≥25,000 read pairs per tissue-covered spot [2]. A 6.5 mm capture area contains 4,992 spots; effective spot count depends on tissue coverage. Visium HD minimum depth scales with preservation method, ≥275M read pairs for FFPE at 100% coverage on 6.5 mm [2]. At least three biological replicates per condition support pseudobulk differential expression; fewer samples can be analyzed with spot-level tests at reduced power.
Yes, with caveats in the QC report. Partial coverage reduces usable spots; depth recommendations scale by capture-area fraction [2]. Degraded FFPE shows lower genes per spot and higher mitochondrial fractions; sub-threshold sections are flagged before domain calling.
Yes. Visium v1/v2 and Visium HD use Space Ranger 3.1.3 from Illumina or MGI DNBSEQ-sequenced FASTQs when scoped at kickoff. Xenium outputs (Prime 5K and smaller panels) are imported with segmentation QC per Salas et al. [3]. CosMx and other platforms are supported on request with client-provided count matrices and coordinates. Custom work is scoped at kickoff.
A standard Visium cohort (4-12 sections, one tissue type, reference deconvolution) typically completes in 4-6 weeks from data receipt. Visium HD bin2cell, multi-slice integration, or Xenium segmentation review may take 6-10 weeks. Timelines are confirmed at kickoff.
Experimental blocking and randomization are preferred at study design. For same-platform cohorts, Harmony corrects technical batches when shared spatial domains are present [16]. Donor, age, and preservation method stay in metadata and are included in pseudobulk models. Cross-platform or cross-slide 3D alignment is scoped separately when rigid coordinate registration is required.
Yes. You retain full ownership of all code, scripts, and results. Pepkio delivers commented R/Python scripts and environment lock files (sessionInfo(), conda, or pip). Objects use standard .h5ad and .rds formats readable in Scanpy or Seurat; R Markdown or Jupyter delivery is available on request.
Yes. Checkpoint reviews occur after QC, domain calling, and deconvolution mapping. You can review domain labels on H&E, adjust clustering resolution, and request contrasts within agreed scope. A dedicated scientific contact leads the project and incorporates your tissue-specific knowledge.
Support covers clarification of computational methods, QC thresholds, and minor figure or table revisions within agreed scope (typically ≤20% of deliverables). Pepkio drafts Methods and Supplementary text for analyses we performed. Substantial new analyses requested by reviewers are scoped separately.
No. Pepkio operates as a fee-for-service provider unless co-authorship is explicitly discussed in advance. Standard practice is acknowledgment of bioinformatics support in the Acknowledgments section.
Not always. Reference-based deconvolution (cell2location, SPOTlight) requires a scRNA-seq atlas from comparable tissue that includes all major cell types present in the section [6]. Domain-level spatial statistics and SVG identification do not require a reference. Reference-free deconvolution is available when no suitable atlas exists; cell-type claims are then interpreted with appropriate caution.
Visium v2 (55 µm spots, multiple cells per spot) suits tissue-domain discovery, niche architecture, and spatial gradients [1, 2]. Visium HD (2 µm bins, cell reconstruction via bin2cell on request) suits questions that need near-single-cell spatial resolution within histological context [17]. Platform choice is usually easier to settle before library prep; Pepkio can advise at kickoff based on your biological question.
Yes, as a scoped milestone. Pepkio can train deconvolution signatures from your scRNA-seq reference and validate abundances against known markers [6, 14]. Joint Visium-Xenium integration on matched sections is available when both datasets exist.
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