Massively parallel identification of single-cell immunophenotypes
Author:
Cienciala Martin,Alvarez Laura,Berne Laura,Chena David,Fikar Pavel,Holubova Monika,Kasl Hynek,Lysak Daniel,Luo Mona,Novackova Zuzana,Ordonez Sheyla,Sramkova Zuzana,Vlas Tomas,Georgiev Daniel
Abstract
AbstractTranslating insights from single-cell analysis into actionable indicators of health and disease requires large-scale confirmatory studies. We introduce biocytometry, a novel method utilizing engineered bioparticles for multiparametric immunophenotyping in suspension, enabling simultaneous measurement across thousands of assays with single-cell sensitivity and a wide dynamic range (1 to 1,000 target cells/sample). The technical validation of biocytometry revealed strong alignment with established technologies (mean bias = 0.25%, LoA = −1.83% to 2.33%) for low-sensitivity settings. Biocytometry excelled in high-sensitivity settings, consistently showcasing superior sensitivity and specificity (LoB = 0), irrespective of the sample type. By employing multiparametric target cell identification, we harnessed the homogeneous assay workflow to discern cell-specific apoptosis in mixed cell cultures. Potential applications include monitoring rare premalignant subpopulations in indications such as smoldering multiple myeloma (SMM), enhancing the detection of circulating tumor cells (CTCs), advancing pharmacokinetic assessments in chimeric antigen receptor (CAR) T-cell therapies, and improving the accuracy of minimal residual disease (MRD) evaluations. Additionally, the high throughput and cell-specific readout capabilities might provide substantial value in drug development, especially for the analysis of complex sample matrices, such as primary cell cultures and organoids.
Publisher
Cold Spring Harbor Laboratory
Reference70 articles.
1. Applying high-dimensional single-cell technologies to the analysis of cancer immunotherapy;Nat. Rev. Clin. Oncol,2020
2. The technological landscape and applications of single-cell multi-omics;Nat. Rev. Mol. Cell Biol,2023
3. Single-cell analysis tools for drug discovery and development;Nat. Rev. Drug Discov,2015
4. Lähnemann, D. et al. Eleven grand challenges in single-cell data science. Genome Biol. 21, 31 (2020).
5. Improving the rigor and reproducibility of flow cytometry-based clinical research and trials through automated data analysis;Cytom. A,2019
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