Optimized Protocol for the Multiomics Processing of Cryopreserved Human Kidney Tissue

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Background: Immediate processing of fresh kidney tissue obtained from needle biopsies is challenging in clinical routines. To address this, tissue samples can be harvested in (cryo-) preservative agents such as RNAlater (Rl) or CellCover (CC), that rapidly preserve the integrity of cellular molecules already at room temperature. In this study, we examined the effects of Rl and CC on the single nucleus transcriptome, on tissue architecture and immunofluorescence stainings and on the proteome. Methods: Biopsy cores from pig kidneys received from a slaughterhouse were either snapfrozen (sf) or put into Rl or CC. For human kidney tissue, the second core of a kidney needle biopsy and healthy kidney tissue from tumor nephrectomies were either sf or put in Rl. For single nucleus RNA sequencing (snRNAseq) with the Chromium 10X platform, 3-4 mm of a thawed kidney biopsy core was dissociated. For proteomic analysis, 2-3 mm sections of biopsies were analyzed by mass spectrometry. For immunohistochemical analysis, samples were thawed and subjected to fluorescent immunolabeling (IF). Results: Each data set from sf, Rl and CC stored pig kidney tissue contributed to 31 clusters of both common and rare kidney cell types. Enrichment analysis detected a similar activation of the stress response pathways in all preservation methods. Applying snRNAseq to human kidney tissue stored in Rl resulted in diverse cell type clustering. Proteome analysis of pig kidney tissues showed the highest correlation between Rl and sf tissues. Classical histology with OCT-embedded kidney tissue as control indicated better preservation in Rl stored kidney compared to CC. IF-staining signal quality in Rl and CC preserved tissue was comparable to OCT-embedded kidney tissue. Conclusion: Our study demonstrates that Rl can facilitate the collection and storage of kidney tissue without the need for snap freezing, supporting snRNAseq, proteome and histopathological analysis.As our optimized protocol requires only 3-4 mm of a biopsy core for high-throughput snRNAseq, the remaining part of a biopsy core can be utilized to generate other omics data.

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