HCA Data Explorer

A transcriptional cross species map of pancreatic islet cells.

Access Granted
Updated February 1, 2024

ObjectivePancreatic islets of Langerhans secrete hormones to regulate systemic glucose levels. Emerging evidence suggests that islet cells are functionally heterogeneous to allow a fine-tuned and efficient endocrine response to physiological changes. A precise description of the molecular basis of this heterogeneity, in particular linking animal models to human islets, is an important step towards identifying the factors critical for endocrine cell function in physiological and pathophysiological conditions.MethodsIn this study, we used single-cell RNA sequencing to profile more than 50'000 endocrine cells isolated from healthy human, pig and mouse pancreatic islets and characterize transcriptional heterogeneity and evolutionary conservation of those cells across the three species. We systematically delineated endocrine cell types and α- and β-cell heterogeneity through prior knowledge- and data-driven gene sets shared across species, which altogether capture common and differential cellular properties, transcriptional dynamics and putative driving factors of state transitions.ResultsWe showed that global endocrine expression profiles correlate, and that critical identity and functional markers are shared between species, while only approximately 20% of cell type enriched expression is conserved. We resolved distinct human α- and β-cell states that form continuous transcriptional landscapes. These states differentially activate maturation and hormone secretion programs, which are related to regulatory hormone receptor expression, signaling pathways and different types of cellular stress responses. Finally, we mapped mouse and pig cells to the human reference and observed that the spectrum of human α- and β-cell heterogeneity and aspects of such functional gene expression are better recapitulated in the pig than mouse data.ConclusionsHere, we provide a high-resolution transcriptional map of healthy human islet cells and their murine and porcine counterparts, which is easily queryable via an online interface. This comprehensive resource informs future efforts that focus on pancreatic endocrine function, failure and regeneration, and enables to assess molecular conservation in islet biology across species for translational purposes.

Heiko LickertInstitute of Diabetes and Regeneration Research, Helmholtz Zentrum München, 85764 Neuherberg, Germany; Institute of Stem Cell Research, Helmholtz Zentrum München, 85764 Neuherberg, Germany; German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany; Technical University of Munich, Medical Faculty, 81675 Munichheiko.lickert@helmholtz-muenchen.de
Fabian J TheisInstitute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany; Technical University of Munich, Department of Mathematics, 85748 Garching b. Munichfabian.theis@helmholtz-muenchen.de
Sophie Tritschler1
Moritz Thomas2
Anika Böttcher3
Barbara Ludwig4
Janine Schmid5
Undine Schubert5
Elisabeth Kemter6
Eckhard Wolf6
Heiko Lickert7
Fabian J Theis8
1Institute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany; Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, 85764 Neuherberg, Germany; Technical University of Munich, School of Life Sciences Weihenstephan, 85354 Freising, Germany.
2Technical University of Munich, School of Life Sciences Weihenstephan, 85354 Freising, Germany; Institute of AI for Health, Helmholtz Zentrum München, 85764 Neuherberg, Germany.
3Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, 85764 Neuherberg, Germany; Institute of Stem Cell Research, Helmholtz Zentrum München, 85764 Neuherberg, Germany.
4Department of Medicine III, University Hospital Carl Gustav Carus, Technical University of Dresden, 01307 Dresden, Germany; Paul Langerhans Institute Dresden of Helmholtz Zentrum München, University Hospital Carl Gustav Carus, Technical University of Dresden, 01307 Dresden, Germany; German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany.
5Department of Medicine III, University Hospital Carl Gustav Carus, Technical University of Dresden, 01307 Dresden, Germany.
6German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany; Chair for Molecular Animal Breeding and Biotechnology, Gene Center, LMU Munich, 81377 Munich, Germany; Center for Innovative Medical Models (CiMM), Department of Veterinary Sciences, LMU Munich, 85764 Oberschleißheim, Germany.
7Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, 85764 Neuherberg, Germany; Institute of Stem Cell Research, Helmholtz Zentrum München, 85764 Neuherberg, Germany; German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany; Technical University of Munich, Medical Faculty, 81675 Munich
8Institute of Computational Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany; Technical University of Munich, Department of Mathematics, 85748 Garching b. Munich
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To reference this project, please use the following link:

https://explore.data.humancellatlas.org/projects/28dd1438-8f40-40d0-8e53-ee3301b66218

Supplementary links are provided by contributors and represent items such as additional data which can’t be hosted here; code that was used to analyze this data; or tools and visualizations associated with this specific dataset.

1.https://cellxgene.cziscience.com/collections/0a77d4c0-d5d0-40f0-aa1a-5e1429bcbd7e
GEO Series Accessions:

Atlas

None

Analysis Portals

None

Project Label

Lickert-Human-10x3pv2

Species

Homo sapiens

Sample Type

specimens

Anatomical Entity

pancreas

Organ Part

islet of Langerhans

Selected Cell Types

Unspecified

Disease Status (Specimen)

normal

Disease Status (Donor)

normal

Development Stage

human adult stage

Library Construction Method

10x 3' v2

Nucleic Acid Source

single cell

Paired End

false

Analysis Protocol

analysis _protocol_2, analysis_protocol_1

File Format

3 file formats

Cell Count Estimate

50.0k

Donor Count

5
fastq.gz10 file(s)h5ad.gz2 file(s)xlsx1 file(s)