Understanding cell-to-cell variation at the molecular level provides relevant information about biological phenomena and is critical for clinical and biological research. Proteins carry important information not available from single-cell genomics and transcriptomics studies; however, due to the minute amount of proteins in single cells and the complexity of the proteome, quantitative protein analysis at the single-cell level remains challenging. Here, we report an integrated microfluidic platform in tandem with matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS) for the detection and quantification of targeted proteins from small cell ensembles (> 10 cells). All necessary steps for the assay are integrated on-chip including cell lysis, protein immunocapture, tryptic digestion, and co-crystallization with the matrix solution for MALDI-MS analysis. We demonstrate that our approach is suitable for protein quantification by assessing the apoptotic protein Bcl-2 released from MCF-7 breast cancer cells, ranging from 26 to 223 cells lysed on-chip (8.75 nL wells). A limit of detection (LOD) of 11.22 nM was determined, equivalent to 5.91 × 10 protein molecules per well. Additionally, the microfluidic platform design was further improved, establishing the successful quantification of Bcl-2 protein from MCF-7 cell ensembles ranging from 8 to 19 cells in 4 nL wells. The LOD in the smaller well designs for Bcl-2 resulted in 14.85 nM, equivalent to 3.57 × 10 protein molecules per well. This work shows the capability of our approach to quantitatively assess proteins from cell lysate on the MIMAS platform for the first time. These results demonstrate our approach constitutes a promising tool for quantitative targeted protein analysis from small cell ensembles down to single cells, with the capability for multiplexing through parallelization and automation.
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http://dx.doi.org/10.1007/s00216-022-04038-y | DOI Listing |
Int J Mol Sci
December 2024
Bioinformatics and Molecular Design Research Center (BMDRC), Incheon 21983, Republic of Korea.
Understanding drug-target interactions is crucial for identifying novel lead compounds, enhancing efficacy, and reducing toxicity. Phenotype-based approaches, like analyzing drug-induced gene expression changes, have shown effectiveness in drug discovery and precision medicine. However, experimentally determining gene expression for all relevant chemicals is impractical, limiting large-scale gene expression-based screening.
View Article and Find Full Text PDFInt J Mol Sci
December 2024
School of Computer Science, University College Dublin (UCD), D04 V1W8 Dublin, Ireland.
Accurately predicting protein secondary structure (PSSP) is crucial for understanding protein function, which is foundational to advancements in drug development, disease treatment, and biotechnology. Researchers gain critical insights into protein folding and function within cells by predicting protein secondary structures. The advent of deep learning models, capable of processing complex sequence data and identifying meaningful patterns, offer substantial potential to enhance the accuracy and efficiency of protein structure predictions.
View Article and Find Full Text PDFBiomed Phys Eng Express
January 2025
School of Engineering and Computing, University of the West of Scotland, University of the West of Scotland - Paisley Campus, Paisley PA1 2BE, UK, City, Paisley, PA1 2BE, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND.
Cancer grade classification is a challenging task identified from the cell structure of healthy and abnormal tissues. The partitioner learns about the malignant cell through the grading and plans the treatment strategy accordingly. A major portion of researchers used DL models for grade classification.
View Article and Find Full Text PDFCells
December 2024
Department of Herbal Pharmacology, College of Korean Medicine, Gachon University, 1342 Seongnamdae-ro, Sujeong-gu, Seongnam-si 13120, Republic of Korea.
The NLRP3 inflammasome, plays a critical role in the pathogenesis of rheumatoid arthritis (RA) by activating inflammatory cytokines such as IL1β and IL18. Targeting NLRP3 has emerged as a promising therapeutic strategy for RA. In this study, a multidisciplinary approach combining machine learning, quantitative structure-activity relationship (QSAR) modeling, structure-activity landscape index (SALI), docking, molecular dynamics (MD), and molecular mechanics Poisson-Boltzmann surface area MM/PBSA assays was employed to identify novel NLRP3 inhibitors.
View Article and Find Full Text PDFCells
December 2024
Neural Dynamics Laboratory, Department of Medicine, The University of Melbourne, Melbourne, VIC 3052, Australia.
Neurological disorders (NDs), such as amyotrophic lateral sclerosis (ALS), Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD), and schizophrenia, represent a complex and multifaceted health challenge that affects millions of people around the world. Growing evidence suggests that disrupted neuronal calcium signalling contributes to the pathophysiology of NDs. Additionally, calcium functions as a ubiquitous second messenger involved in diverse cellular processes, from synaptic activity to intercellular communication, making it a potential therapeutic target.
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