Towards a consensus atlas of human and mouse adipose tissue at single-cell resolution

Abstract

Adipose tissue (AT) is a complex connective tissue with a high relative proportion of adipocytes, which are specialized cells with the ability to store lipids in large droplets. AT is found in multiple discrete depots throughout the body, where it serves as the primary repository for excess calories. In addition, AT has an important role in functions as diverse as insulation, immunity and regulation of metabolic homeostasis. The Human Cell Atlas Adipose Bionetwork was established to support the generation of single-cell atlases of human AT as well as the development of unified approaches and consensus for cell annotation. Here, we provide a first roadmap from this bionetwork, including our suggested cell annotations for humans and mice, with the aim of describing the state of the field and providing guidelines for the production, analysis, interpretation and presentation of AT single-cell data.

Figure 1.. Overview of main adipose depots in human and mouse.

The figure illustrates the approximate anatomical locations of key adipose depots in humans [A] and mice [B]. The colour of the adipose depot links to its classification as white, beige, or brown fat.

Figure 2.. Workflow for single-cell and single-nucleus analysis on human AT using the 10X Genomics platform.

Adipose tissue samples are collected from patients with informed consent, and metadata, including subject demographics and clinical data, are recorded. For snRNA-seq, adipose tissue can be snap-frozen, allowing nuclei to be extracted from frozen samples. For scRNA-seq, fresh tissue is preferred, but fresh-frozen tissue can also be used if dissociated into single-cell suspensions before freezing. The samples are then processed to isolate cells or nuclei through enzymatic digestion or mechanical dissociation. Sample barcoding for multiplexing can be employed to pool multiple samples in a single sequencing run, reducing costs and increasing throughput. Flow cytometry can be used to further sort and enrich specific cell populations before sequencing, or to remove low-quality nuclei or cells. Finally, the prepared samples are loaded onto the 10x Chromium Controller, which uses advanced microfluidics to partition individual cells or nuclei into droplets, each containing a unique barcode for downstream sequencing and analysis.

Figure 3.. Overview of computational workflow for single-cell analyses on human AT.

The workflow begins with aligning reads to the genome using Cellranger. Ambient RNA is removed using tools like Cellbender, SoupX, or DecontX and doublets are identified and removed using scDblFinder, scdx, DoubletFinder, or scrublet. Cells and genes are filtered based on quality control metrics. Data is then combined and integrated using Seurat or scanpy, with integration methods such as Harmony, CCA, and RPCA. Clusters and marker genes are identified using Seurat, Scanpy, or Liger. Data can be mapped to reference datasets using Azimuth or CellTypist. Additional analyses include trajectory analysis with slingshot or monocole3, differential expression analysis using pseudobulk, and prediction of cell-cell interactions with CellphoneDB or Cellchat. Listed programs are suggestions and not exhaustive.