Spatial Transcriptomics Reveals a Myeloma Cell Architecture with Dysfunctional T-Cell Distribution, Neutrophil Traps, and Inflammatory Signaling

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: July 5, 2024

ABSTRACT The bone marrow (BM) is a complex tissue where spatial relationships influence cell behavior, signaling, and function. Consequently, understanding the whole dynamics of cellular interactions requires complementary techniques that preserve map architecture populations in situ . We successfully conducted transcriptional profiling using Visium Spatial Gene Expression formalin-fixed paraffin-embedded (FFPE) BM samples obtained from healthy Multiple Myeloma (MM) mouse models patients, addressing technical challenges applying technology to long samples. A custom data-analysis framework combines with single-cell transcriptomic profiles identified both composition existing relations. This allowed us visualize distribution transcriptionally heterogeneous MM plasma cells (MM-PC). spatially delineated programs associated MM, including NETosis IL-17-driven inflammatory which were inversely correlated malignant PC-enriched regions. Furthermore, gradient MM-PC density shift effector-to-exhausted T phenotypes. translational relevance our findings was confirmed FFPE biopsies patients varying levels PC infiltration. In summary, we provide first transcriptomics analysis applied human mineralized illustrate revealing deregulated mechanisms underlying intercellular communication.

Language: Английский

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CelloType: A Unified Model for Segmentation and Classification of Tissue Images

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 19, 2024

Abstract Cell segmentation and classification are critical tasks in spatial omics data analysis. We introduce CelloType, an end-to-end model designed for cell of biomedical microscopy images. Unlike the traditional two-stage approach followed by classification, CelloType adopts a multi-task learning that connects simultaneously boost performance both tasks. leverages Transformer-based deep techniques enhanced accuracy object detection, segmentation, classification. It outperforms existing methods using ground-truths from public databases. In terms baseline comprised state-of-the-art individual Using multiplexed tissue images, we further demonstrate utility multi-scale cellular non-cellular elements tissue. The multi-task-learning ability facilitate automated annotation rapidly growing data.

Language: Английский

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Recent advancement in the spatial immuno-oncology

Seminars in Cell and Developmental Biology, Journal Year: 2024, Volume and Issue: 166, P. 22 - 28

Published: Dec. 19, 2024

Language: Английский

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Φ-Space: Continuous phenotyping of single-cell multi-omics data

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: June 22, 2024

Abstract Single-cell multi-omics technologies have empowered increasingly refined characterisation of the heterogeneity cell populations. Automated type annotation methods been developed to transfer labels from well-annotated reference datasets emerging query datasets. However, these suffer some common caveats, including failure characterise transitional and novel states, sensitivity batch effects under-utilisation phenotypic information other than types (e.g. sample source disease conditions). We Φ-Space, a computational framework for continuous phenotyping single-cell data. In Φ-Space we adopt highly versatile modelling strategy continuously identity in low-dimensional phenotype space, defined by phenotypes. The space embedding enables various downstream analyses, insightful visualisations, clustering labelling. demonstrate through three case studies that (i) characterises developing out-of-reference states; (ii) is robust against both query; (iii) adapts tasks involving multiple omics types; (iv) overcomes technical differences between query. versatility makes it applicable wide range analytical beyond transfer, its ability model complex variation will facilitate biological discoveries different types.

Language: Английский

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Reconsidering the usual suspects in age-related hematologic disorders: is stem cell dysfunction a root cause of aging?

Experimental Hematology, Journal Year: 2024, Volume and Issue: unknown, P. 104698 - 104698

Published: Dec. 1, 2024

Language: Английский

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