Strategies for Integrating Single-Cell RNA Sequencing Results With Multiple Species

Abstract

Single-cell RNA sequencing (scRNAseq) is a robust technology for parsing gene expression in individual cells from a tissue or other complex source. One application involves experiments where cells from multiple species are recovered from a single sample, such as when human cells are transplanted into an animal model. We transplanted microglial precursor cells into newborn mouse brain and then recovered unenriched cortical tissue six months later. Dissociated cells were assessed by scRNAseq. The default method for analyzing these results begins by aligning sequencing reads with a mixture of both mouse and human reference genomes. While this clearly identifies the human cells as a distinct cluster, the clustering is artificially driven by expression from non-comparable gene identifiers from different species. We devised a method for translating expression counts from human to mouse and evaluated four algorithms for parsing mixed-species scRNAseq data. Our optimal approach split raw sequencing reads according to the best alignment score in each genome, and then re-aligned reads only with the appropriate genome. After gene symbol translation, pooled results indicate that cell types are more appropriately clustered and that differential expression analysis identifies species-specific patterns. This method should be applicable to any mixed-species scRNAseq experiment. Summary of optimal strategy:• Mixed-species scRNAseq data are aligned with mixture of mouse and human reference genomes • The BAM file is scanned to find the best alignment score for each sequencing read identifier; these are used to split the paired FASTQ files into two sets of files • Each set of species-specific, paired FASTQ files is re-aligned with only the appropriate reference genome • Raw counts imported into Seurat • The human counts table is translated to mouse gene symbols using a custom HomoloGene translation table • Results are merged and analyzed

Human iPSC-derived mature microglia retain their identity and functionally integrate in the chimeric mouse brain

“…Sample/ barcode identifiers not matching this cluster were assumed to be mouse, and these were trimmed to retain only mouse gene symbols matching the homology list. Complete details of homology gene translation are described elsewhere 79 .…”

“…In the end, only 147 out of 19,154 barcodes, or 0.76%, included sequences optimally aligning with both species, likely caused by the creation of droplets containing cells from more than one species, so these were eliminated from further consideration. The entire procedure, along with comparisons with alternative strategies, including all required R and Python code, is described elsewhere 79 .…”

Interspecies Single‐Cell RNA‐Seq Analysis Reveals the Novel Trajectory of Osteoclast Differentiation and Therapeutic Targets

“…(18) For the interspecies analysis, gene symbols were transformed to the corresponding homolog gene names using a gene conversion table called geneTrans. (19) Human and humanized murine or murinized human and murine data sets were merged, then the merged data sets were divided according to their origin and were regressed out of cell-cycle scores and expressed mitochondrial gene's proportion. After that, the two divided data sets were integrated based on 5000 common anchoring genes selected using the "SelectIntegrationFeatures" command.…”

Comparative single-cell analyses identify shared and divergent features of human and mouse kidney development

“…First, human and mouse N-UE sc/snRNA-seq cells were integrated on the basis of common orthologous variable features. Briefly, mouse gene symbols in mouse scRNA-seq Seurat objects were renamed to human orthologous gene symbols 116 . The CCA-based integration was performed with 3,000 most variable features selected by SelectIntegrationFeatures function, then cross-dataset pairs of cells in matched cell-types, i.e., 'anchors', were identified FindIntegrationAnchors function.…”