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Analyzing proteins from single cells by tandem mass spectrometry (MS) has recently become technically feasible. While such analysis has the potential to accurately quantify thousands of proteins across thousands of single cells, the accuracy and reproducibility of the results may be undermined by numerous factors affecting experimental design, sample preparation, data acquisition and data analysis. We expect that broadly accepted community guidelines and standardized metrics will enhance rigor, data quality and alignment between laboratories. Here we propose best practices, quality controls and data-reporting recommendations to assist in the broad adoption of reliable quantitative workflows for single-cell proteomics. Resources and discussion forums are available at https://single-cell.net/guidelines .
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10130941 | PMC |
http://dx.doi.org/10.1038/s41592-023-01785-3 | DOI Listing |
Nat Aging
September 2025
Aging Biomarker Consortium (ABC), Beijing, China.
The global surge in the population of people 60 years and older, including that in China, challenges healthcare systems with rising age-related diseases. To address this demographic change, the Aging Biomarker Consortium (ABC) has launched the X-Age Project to develop a comprehensive aging evaluation system tailored to the Chinese population. Our goal is to identify robust biomarkers and construct composite aging clocks that capture biological age, defined as an individual's physiological and molecular state, across diverse Chinese cohorts.
View Article and Find Full Text PDFVirchows Arch
September 2025
Department of Anatomic Pathology, Tohoku University Graduate School of Medicine, Sendai, Japan.
Lung adenocarcinoma (LUAD) associated with usual interstitial pneumonia (UIP) harbours distinct features compared to lung adenocarcinoma without UIP. Therefore, we aimed to characterise the tumour microenvironment of LUAD with UIP by focusing on cancer-associated fibroblasts (CAFs) and stromal composition. Immunohistochemistry was performed on 32 LUAD samples (16 each with and without UIP) to evaluate CAF marker expression and lymphocyte infiltration.
View Article and Find Full Text PDFCancer Biol Med
September 2025
State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers, Peking University Cancer Hospital & Institute, Beijing 100142, China.
Objective: The key molecular events signifying the -induced gastric carcinogenesis process are largely unknown.
Methods: Bulk tissue-proteomics profiling were leveraged across multi-stage gastric lesions from Linqu ( = 166) and Beijing sets ( = 99) and single-cell transcriptomic profiling ( = 18) to decipher key molecular signatures of -related gastric lesion progression and gastric cancer (GC) development. The association of key proteins association with gastric lesion progression and GC development were prospectively studied building on follow-up of the Linqu set and UK Biobank ( = 48,529).
Bioinform Adv
August 2025
Mount Sinai Institute for Systems Biomedicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States.
Motivation: Advances in high-throughput technologies have shifted the focus from bulk to single cell or spatial transcriptomic and proteomic analysis of tissues and cell cultures. The resulting increase in gene and/or protein lists leads to the subsequent growth of up- and downregulated pathways lists. This trend creates the need for pathway-network based integration strategies that allow quick exploration of shared and distinct mechanisms across datasets.
View Article and Find Full Text PDFAdv Sci (Weinh)
September 2025
School of Artificial Intelligence, Jilin University, Changchun, 130012, China.
Single-cell multi-omics technologies are pivotal for deciphering the complexities of biological systems, with Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) emerging as a particularly valuable approach. The dual-modality capability makes CITE-seq particularly advantageous for dissecting cellular heterogeneity and understanding the dynamic interplay between transcriptomic and proteomic landscapes. However, existing computational models for integrating these two modalities often struggle to capture the complex, non-linear interactions between RNA and antibody-derived tags (ADTs), and are computationally intensive.
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