Collections / Integrated spatial multi-omics reveals fibroblast fate during tissue repair

Integrated spatial multi-omics reveals fibroblast fate during tissue repair

In the skin, tissue injury results in fibrosis in the form of scars composed of dense extracellular matrix deposited by fibroblasts. The therapeutic goal of regenerative wound healing has remained elusive in part because principles of fibroblast programming and adaptive response to injury remain incompletely understood. Here, we present a multimodal -omics platform for the comprehensive study of cell populations in complex tissue, which has allowed us to characterize the cells involved in wound healing across both time and space. We employ a stented wound model that recapitulates human tissue repair kinetics and multiple Rainbow transgenic lines to precisely track fibroblast fate during the physiologic response to injury. Through integrated analysis of single cell chromatin landscapes and gene expression states, coupled with spatial transcriptomic profiling, we are able to impute fibroblast epigenomes with temporospatial resolution. This has allowed us to define the mechanisms controlling cell fate during migration, proliferation, and differentiation following tissue injury and thereby reexamine the canonical phases of wound healing. We show that wounding triggers a polyclonal proliferation of tissue-resident, mechano-responsive fibroblasts, the subpopulations of which inhabit spatially-distinct regions within the wound and harbor distinct activities. These findings have broad implications for the study of tissue repair in complex organ systems.

Datasets4
Size~97 MB
AssayVisium
Samplemouse · skin of body
KindTime course
PMID:34620713 ↗
Python · lamindb
import lamindb as ln
import spatialdata as sd

coll = ln.Collection.get(key="geo-gse178758")
coll.ordered_artifacts.df()                 # the 4 members, ordered

# download every member .zarr to the local cache:
paths = [a.cache() for a in coll.ordered_artifacts.all()]

# …or load each straight into memory as a SpatialData object:
sdatas = {a.key: sd.read_zarr(a.cache()) for a in coll.ordered_artifacts.all()}
Open in Vitessce
Visium
Mouse skin of body
GSM6443210
mouse skin of body 17 MB 4,992 obs bronze H&E
TechnologyVisium
Organismmouse
Tissueskin of body
Size17 MB
Obs4,992
Magnificationother · 4.053 µm/px
bronze H&E Open in Vitessce
Open in Vitessce
Visium
Mouse skin of body
GSM6443211
mouse skin of body 12 MB 4,992 obs bronze H&E
TechnologyVisium
Organismmouse
Tissueskin of body
Size12 MB
Obs4,992
bronze H&E Open in Vitessce
Open in Vitessce
Visium
Mouse skin of body
GSM6443213
mouse skin of body 38 MB 4,992 obs bronze H&E
TechnologyVisium
Organismmouse
Tissueskin of body
Size38 MB
Obs4,992
Magnificationother · 3.681 µm/px
bronze H&E Open in Vitessce
Open in Vitessce
Visium
Mouse skin of body
GSM6443212
mouse skin of body 30 MB 4,992 obs bronze H&E
TechnologyVisium
Organismmouse
Tissueskin of body
Size30 MB
Obs4,992
Magnificationother · 4.046 µm/px
bronze H&E Open in Vitessce

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