FixNCut: Single-cell genomics through reversible tissue fixation and dissociation

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The use of single-cell technologies for clinical applications requires disconnecting sampling from downstream processing steps. Early sample preservation can further increase robustness and reproducibility by avoiding artifacts introduced during specimen handling. We present FixNCut, a methodology for the reversible fixation of tissue followed by dissociation that overcomes current limitations. We applied FixNCut to human and mouse tissues to demonstrate the preservation of RNA integrity, sequencing library complexity, and cellular composition, while diminishing stress-related artifacts. Besides single-cell RNA sequencing, FixNCut is compatible with multiple single-cell and spatial technologies, making it a versatile tool for robust and flexible study designs.

This upload contains:

• FIXnCUT_metadata.csv: Comma-separated text file with the metadata information for each of the single-cell RNA-seq libraries (3'GEX v3.1) we have generated and included in the manuscript.

• FIXnCUT_HumanPBMC_clustering_annotation_cleaned.rds: An RDS file containing the Seurat object with the annotated Human PBMC data, for both Fresh and Fixed samples.
• FIXnCUT_MouseLung_fixed_clustering_annotation_cleaned.rds: An RDS file containing the Seurat object with the annotated Mouse Lung (fixed) data, for both Fresh and Fixed tissue samples from two biological replicates.
• FIXnCUT_MouseColon_filtered_post_clustering_annotation.rds: An RDS file containing the Seurat object with the annotated Mouse Colon data, for both Fresh and Fixed tissue samples.
• FIXnCUT_MouseLung_cryopreserved_clustering_annotation_cleaned.rds:  An RDS file containing the Seurat object with the annotated Mouse Lung (cryopreserved) data, for Fixed, Cryopreserved and Fixed+Cryopreserved tissue samples.
• FIXnCUT_HumanColon_filtered_clustering_annotation_cleaned.rds: An RDS file containing the Seurat object with the annotated Human Colon data from IBD patients, for Fresh, Fixed, Cryopreserved and Fixed+Cryopreserved tissue biopsies.

R objectes (RDS files) can be read in R using Seurat. All the files contain:

• "RNA assay" with the counts and LogNormalize data matrices,
• The computed 3000 HVG genes
• PCA and UMAP embeddings, and also Harmonized UMAP embeddings (when integration was applied).

Additionally, the metadata includes information for the following variables:

• GEM_id / library_name
• Sample-related information, such as: tissue, disease, sample_protocol
• Cell-related information, such as: scrublet doublet scores / predictions, nCounts_RNA, nFeature_RNA, pct_mt, pct_rb, S.Score, G2M.Score, Phase
• Clustering and annotation information: multiple resolutions (computed and explored), as well as the final lineage annotation and cell-type assigned to each cluster, for the selected resolution.

For more information, check out the GitHub repository: FIXnCUT_benchmarking.