Integration of Mouse and Human Single-cell RNA Sequencing Infers Spatial Cell-type Composition in Human Brains

Ts, Johnson; Zb, Abrams; Br, Helm; Neidecker, Peter; Machiraju, Raghu; Y, Zhang; Huang, Kun; Zhang, J

doi:10.1101/527499

Cited by 3 publications

(2 citation statements)

References 39 publications

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“…Our goal was to process a dataset from two species. In another example, mouse and human brain scRNAseq data have been integrated for a combined cell-type analysis (Johnson et al, 2019), but this is a different design.…”

Section: Introductionmentioning

confidence: 99%

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

Hart

2019

Preprint

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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

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Section: Introductionmentioning

confidence: 99%

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

Hart

2019

Preprint

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“…Based on this evidence, the combination of human and mouse single-cell trascriptomic profiles, through the application of feature selection and linear modeling, was used to provide better insights into human brain connectivity. Then, the combined data were used to demonstrate that gene expression is a better indicator of cellular localization than the location of cell nuclei, especially for cells with large and irregularly shaped cell bodies such as the neurons [15].…”

Section: Introductionmentioning

confidence: 99%

Exploiting Gene Expression Profiles for the Automated Prediction of Connectivity between Brain Regions

Roberti

Lovino

Cataldo

et al. 2019

IJMS

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The brain comprises a complex system of neurons interconnected by an intricate network of anatomical links. While recent studies demonstrated the correlation between anatomical connectivity patterns and gene expression of neurons, using transcriptomic information to automatically predict such patterns is still an open challenge. In this work, we present a completely data-driven approach relying on machine learning (i.e., neural networks) to learn the anatomical connection directly from a training set of gene expression data. To do so, we combined gene expression and connectivity data from the Allen Mouse Brain Atlas to generate thousands of gene expression profile pairs from different brain regions. To each pair, we assigned a label describing the physical connection between the corresponding brain regions. Then, we exploited these data to train neural networks, designed to predict brain area connectivity. We assessed our solution on two prediction problems (with three and two connectivity class categories) involving cortical and cerebellum regions. As demonstrated by our results, we distinguish between connected and unconnected regions with 85% prediction accuracy and good balance of precision and recall. In our future work we may extend the analysis to more complex brain structures and consider RNA-Seq data as additional input to our model.

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Strategies for integrating single-cell RNA sequencing results with multiple species

Hart¹

2019

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Integration of Mouse and Human Single-cell RNA Sequencing Infers Spatial Cell-type Composition in Human Brains

Cited by 3 publications

References 39 publications

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

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

Exploiting Gene Expression Profiles for the Automated Prediction of Connectivity between Brain Regions

Strategies for integrating single-cell RNA sequencing results with multiple species

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