Biology subjects

Chaerkady, R.

Publications and source records attributed to Chaerkady, R..

3 recordsLinked to original sources

bioRxiv2025

Deep Blood Proteomics Identifies over 12,000 Proteins Providing Valuable Information about the State of the Human Body

Blood is a valuable resource for clinical research, offering insight into physiological and pathological states. However, the specific proteins detectable in blood and the optimal proteomic methods for their detection have not been rigorously investigated and documented. To address this, we conducted various blood proteomic strategies, including directly blood proteomic analysis, high-abundance protein depletion, low-abundance protein enrichment, and extracellular vesicle enrichment using data-independent acquisition or targeted proteomics. These approaches identified 11,679 protein groups in plasma from healthy individuals. In 136 pancreatic ductal adenocarcinoma whole blood samples, 6,956 protein groups were found, including 678 not seen in healthy samples, expanding the total to 12,357 blood proteins. This represents the most comprehensive blood proteome to date. To support broader access and analysis, we developed the Human Blood Proteome (HuBP) database, detailing protein detectability, abundance, and reproducibility across workflows, sample types, and disease contexts.

systems biology↗

bioRxiv2025

Complete360: A Multi-Omics Liquid Biopsy Platform with Attomole Sensitivity and High Reproducibility

Advancements in liquid biopsy platforms can uncovered disease-associated molecular changes that may indicate disease. Despite recent progress in the detection of a number of analytes, the current liquid biopsy platforms struggle with specificity, sensitivity, and reproducibility, limiting their clinical utility and high-throughput applicability. Here, we describe an ultra-sensitive mass spectrometry-based liquid biopsy platform, Complete360, with an optimized library of detection parameters, meticulously refined assays to enable the simultaneous detection and quantification of a broad range of body-fluid proteins, metabolites, and lipids. The system addresses sample-specific and target-specific noises, as well as the micro-environments of proteomics during co-elution, achieving unprecedented levels of reproducibility, specificity, and sensitivity. This platform has facilitated the detection and quantification of numerous multi-omics signatures in human plasma samples, uncovering novel disease mechanisms never identified by previous approaches. Our study establishes a platform for rapid discovery and validation of molecular changes in disease panels, demonstrating its potential utilities in applications meeting clinical regulation guidelines.

systems biology↗

bioRxiv2018

Proteogenomic annotation of the Chinese hamster reveals extensive novel translation events and endogenous retroviral elements

A high quality genome annotation greatly facilitates successful cell line engineering. Standard draft genome annotation pipelines are based largely on de novo gene prediction, homology, and RNA-Seq data. However, draft annotations can suffer from incorrectly predictions of translated sequence, incorrect splice isoforms and missing genes. Here we generated a draft annotation for the newly assembled Chinese hamster genome and used RNA-Seq, proteomics, and Ribo-Seq to experimentally annotate the genome. We identified 4,333 new proteins compared to the hamster RefSeq protein annotation and 2,503 novel translational events (e.g., alternative splices, mutations, novel splices). Finally, we used this pipeline to identify the source of translated retroviruses contaminating recombinant products from Chinese hamster ovary (CHO) cell lines, including 131 type-C retroviruses, thus enabling future efforts to eliminate retroviruses by reducing the costs incurred with retroviral particle clearance. In summary, the improved annotation provides a more accurate platform for guiding CHO cell line engineering, including facilitating the interpretation of omics data, defining of cellular pathways, and engineering of complex phenotypes.

bioinformatics↗