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AnnoSpat annotates cell types and quantifies cellular arrangements from spatial proteomics.

ABSTRACT: Cellular composition and anatomical organization influence normal and aberrant organ functions. Emerging spatial single-cell proteomic assays such as Image Mass Cytometry (IMC) and Co-Detection by Indexing (CODEX) have facilitated the study of cellular composition and organization by enabling high-throughput measurement of cells and their localization directly in intact tissues. However, annotation of cell types and quantification of their relative localization in tissues remain challenging. To address these unmet needs, we developed AnnoSpat (Annotator and Spatial Pattern Finder) that uses neural network and point process algorithms to automatically identify cell types and quantify cell-cell proximity relationships. Our study of data from IMC and CODEX show the superior performance of AnnoSpat in rapid and accurate annotation of cell types compared to alternative approaches. Moreover, the application of AnnoSpat to type 1 diabetic, non-diabetic autoantibody-positive, and non-diabetic organ donor cohorts recapitulated known islet pathobiology and showed differential dynamics of pancreatic polypeptide (PP) cell abundance and CD8⁺ T cells infiltration in islets during type 1 diabetes progression.

SUBMITTER: Mongia A

PROVIDER: S-EPMC9882100 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature

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AnnoSpat annotates cell types and quantifies cellular arrangements from spatial proteomics.

Mongia Aanchal A Saunders Diane C DC Wang Yue J YJ Brissova Marcela M Powers Alvin C AC Kaestner Klaus H KH Vahedi Golnaz G Naji Ali A Schwartz Gregory W GW Faryabi Robert B RB

bioRxiv : the preprint server for biology 20230118

Cellular composition and anatomical organization influence normal and aberrant organ functions. Emerging spatial single-cell proteomic assays such as Image Mass Cytometry (IMC) and Co-Detection by Indexing (CODEX) have facilitated the study of cellular composition and organization by enabling high-throughput measurement of cells and their localization directly in intact tissues. However, annotation of cell types and quantification of their relative localization in tissues remain challenging. To ...[more]

PMID: 36712052

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Project description:Most of the recombinant biotherapeutics employed today to combat severe illnesses, for example, various types of cancer or autoimmune diseases, are produced by Chinese hamster ovary (CHO) cells. To meet the growing demand of these pharmaceuticals, CHO cells are under constant development in order to enhance their stability and productivity. The last decades saw a shift from empirical cell line optimization toward rational cell engineering using a growing number of large omics datasets to alter cell physiology on various levels. Especially proteomics workflows reached new levels in proteome coverage and data quality because of advances in high-resolution mass spectrometry instrumentation. One type of workflow concentrates on spatial proteomics by usage of subcellular fractionation of organelles with subsequent shotgun mass spectrometry proteomics and machine learning algorithms to determine the subcellular localization of large portions of the cellular proteome at a certain time point. Here, we present the first subcellular spatial proteome of a CHO-K1 cell line producing high titers of recombinant antibody in comparison to the spatial proteome of an antibody-producing plasma cell-derived myeloma cell line. Both cell lines show colocalization of immunoglobulin G chains with chaperones and proteins associated in protein glycosylation within the endoplasmic reticulum compartment. However, we report differences in the localization of proteins associated to vesicle-mediated transport, transcription, and translation, which may affect antibody production in both cell lines. Furthermore, pairing subcellular localization data with protein expression data revealed elevated protein masses for organelles in the secretory pathway in plasma cell-derived MPC-11 (Merwin plasma cell tumor-11) cells. Our study highlights the potential of subcellular spatial proteomics combined with protein expression as potent workflow to identify characteristics of highly efficient recombinant protein-expressing cell lines. Data are available via ProteomeXchange with identifier PXD029115.

Project description:BACKGROUND:Pulmonary fibrosis is a respiratory disease caused by the proliferation of fibroblasts and accumulation of the extracellular matrix (ECM). It is known that the lung ECM is mainly composed of a three-dimensional fiber mesh filled with various high-molecular-weight proteins. However, the small-molecular-weight proteins in the lung ECM and their differences between normal and fibrotic lung ECM are largely unknown. METHODS:Healthy adult male Sprague-Dawley rats (Rattus norvegicus) weighing about 150 to 200 g were randomly divided into three groups using random number table: A, B, and C and each group contained five rats. The rats in Group A were administered a single intragastric (i.g.) dose of 500 ?L of saline as control, and those in Groups B and C were administered a single i.g. dose of paraquat (PQ) dissolved in 500 ?L of saline (20 mg/kg). After 2 weeks, the lungs of rats in Group B were harvested for histological observation, preparation of de-cellularized lung scaffolds, and proteomic analysis for small-molecular-weight proteins, and similar procedures were performed on Group C and A after 4 weeks. The differentially expressed small-molecular-weight proteins (DESMPs) between different groups and the subcellular locations were analyzed. RESULTS:Of the 1626 small-molecular-weight proteins identified, 1047 were quantifiable. There were 97 up-regulated and 45 down-regulated proteins in B vs. A, 274 up-regulated and 31 down-regulated proteins in C vs. A, and 237 up-regulated and 28 down-regulated proteins identified in C vs. B. Both the up-regulated and down-regulated proteins in the three comparisons were mainly distributed in single-organism processes and cellular processes within biological process, cell and organelle within cellular component, and binding within molecular function. Further, more up-regulated than down-regulated proteins were identified in most sub-cellular locations. The interactions of DESMPs identified in extracellular location in all comparisons showed that serum albumin (Alb) harbored the highest degree of node (25), followed by prolyl 4-hydroxylase beta polypeptide (12), integrin ?1 (10), apolipoprotein A1 (9), and fibrinogen gamma chain (9). CONCLUSIONS:Numerous PQ-induced DESMPs were identified in de-cellularized lungs of rats by high throughput proteomics analysis. The DESMPs between the control and treatment groups showed diversity in molecular functions, biological processes, and pathways. In addition, the interactions of extracellular DESMPs suggested that the extracellular proteins Alb, Itgb1, Apoa1, P4hb, and Fgg in ECM could be potentially used as biomarker candidates for pulmonary fibrosis. These results provided useful information and new insights regarding pulmonary fibrosis.

Dataset Information

AnnoSpat annotates cell types and quantifies cellular arrangements from spatial proteomics.

Publications

AnnoSpat annotates cell types and quantifies cellular arrangements from spatial proteomics.

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