Unsupervised analysis of flow cytometry data in a clinical setting captures cell diversity and allows population discovery
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA680799 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:In our study, MegaClust - an unsupervised, data-driven algorithm - barely described mDCs and pDC subsets were identified. To confirm these findings we performed RNA sequencing Facs sorted of mDCs (CD123-CD11c+CD4+HLA-DR+), pDCs (CD123+CD11c-CD4+HLA-DR+) and monocytes (CD14+) from healthy donors and compared these with publicly available data.
2021-04-27 | GSE162177 | GEO
Project description:Data obtained with cytometry are increasingly complex and their interrogation impacts the type and quality of knowledge gained. Conventional supervised analyses are limited to pre-defined cell populations and do not exploit the full potential of data. Here, in the context of a clinical trial of cancer patients treated with radiotherapy, we performed longitudinal flow cytometry analyses to identify multiple distinct cell populations in circulating whole blood. We cross-compared the results from state-of-the-art recommended supervised analyses with results from MegaClust, a high-performance data-driven clustering algorithm allowing fast and robust identification of cell-type populations. Ten distinct cell populations were accurately identified by supervised analyses, including main T, B, dendritic cell (DC), natural killer (NK) and monocytes subsets. While all ten subsets were also identified with MegaClust, additional cell populations were revealed (e.g. CD4+HLA-DR+ and NKT-like subsets), and DC profiling was enriched by the assignment of additional subset-specific markers. Comparison between transcriptomic profiles of purified DC populations and publicly available datasets confirmed the accuracy of the unsupervised clustering algorithm and demonstrated its potential to identify rare and scarcely described cell subsets. Our observations show that data-driven analyses of cytometry data significantly enrich the amount and quality of knowledge gained, representing an important step in refining the characterization of immune responses.
| S-EPMC8119773 | biostudies-literature
Project description:This study was designed to investigate the efficacy of flow cytometry to accurately identify between normal and cancer cells in colon epithelium in humans diagnosed with colorectal cancer.
| 2406350 | ecrin-mdr-crc
Project description:Human Bone Marrow Assessment by Single Cell RNA Sequencing, Mass Cytometry and Flow Cytometry
| PRJNA493078 | ENA
Project description:New techniques for single-cell analysis have led to insights into hematopoiesis and the immune system, but the ability of these techniques to cross-validate and reproducibly identify the biological variation in diverse human samples is currently unproven. We therefore performed a comprehensive assessment of human bone marrow cells using both single-cell RNA sequencing and multiparameter flow cytometry from twenty healthy adult human donors across a broad age range. These data characterize variation between healthy donors as well as age-associated changes in cell population frequencies. Direct comparison of techniques revealed discrepancy in the quantification of T lymphocyte and natural killer cell populations. Orthogonal validation of immunophenotyping using mass cytometry demonstrated good correlation with flow cytometry. Technical replicates using single-cell RNA sequencing matched robustly, while biological replicates showed variation. Given the increasing use of single-cell technologies in translational research, this resource serves as an important reference dataset and highlights opportunities for further refinement. [Funding source] Project Number: 1ZIAHL006163-05 Contact PI / Project Leader: HOURIGAN, CHRISTOPHER Title: DETECTION, PREVENTION AND TREATMENT OF ACUTE MYELOID LEUKEMIA (AML) RELAPSE. Awardee Organization: NATIONAL HEART, LUNG, AND BLOOD INSTITUTE
2018-09-21 | GSE120221 | GEO
Project description:Human Bone Marrow Assessment by Single Cell RNA Sequencing, Mass Cytometry and Flow Cytometry [scRNA]
| PRJNA492227 | ENA
Project description:Human Bone Marrow Assessment by Single Cell RNA Sequencing, Mass Cytometry and Flow Cytometry [bulk]
| PRJNA493081 | ENA
Project description:BackgroundThere are many important clustering questions in computational biology for which no satisfactory method exists. Automated clustering algorithms, when applied to large, multidimensional datasets, such as flow cytometry data, prove unsatisfactory in terms of speed, problems with local minima or cluster shape bias. Model-based approaches are restricted by the assumptions of the fitting functions. Furthermore, model based clustering requires serial clustering for all cluster numbers within a user defined interval. The final cluster number is then selected by various criteria. These supervised serial clustering methods are time consuming and frequently different criteria result in different optimal cluster numbers. Various unsupervised heuristic approaches that have been developed such as affinity propagation are too expensive to be applied to datasets on the order of 106 points that are often generated by high throughput experiments.ResultsTo circumvent these limitations, we developed a new, unsupervised density contour clustering algorithm, called Misty Mountain, that is based on percolation theory and that efficiently analyzes large data sets. The approach can be envisioned as a progressive top-down removal of clouds covering a data histogram relief map to identify clusters by the appearance of statistically distinct peaks and ridges. This is a parallel clustering method that finds every cluster after analyzing only once the cross sections of the histogram. The overall run time for the composite steps of the algorithm increases linearly by the number of data points. The clustering of 106 data points in 2D data space takes place within about 15 seconds on a standard laptop PC. Comparison of the performance of this algorithm with other state of the art automated flow cytometry gating methods indicate that Misty Mountain provides substantial improvements in both run time and in the accuracy of cluster assignment.ConclusionsMisty Mountain is fast, unbiased for cluster shape, identifies stable clusters and is robust to noise. It provides a useful, general solution for multidimensional clustering problems. We demonstrate its suitability for automated gating of flow cytometry data.
| S-EPMC2967560 | biostudies-literature
Project description:flow cytometry sorted photosynthetic picoeukaryotes in freshwater lakes Raw sequence reads
| PRJNA419134 | ENA
Project description:Tumorigenic breast cancer cells characterized by CD44 expression and low or undetectable CD24 levels (CD44+/CD24-/low) may be resistant to chemotherapy and therefore responsible for cancer relapse. Paired breast cancer core biopsies before and after neoadjuvant chemotherapy or lapatinib were obtained and as single cell suspensions stained using antibodies against CD24, CD44, and lineage markers, and then analyzed by flow cytometry. Mammosphere (MS) formation in culture was compared before and after treatment. Global gene expression differences between cancer cells bearing CD44+/CD24-/low cells and all other sorted cells, and between cancer MS and the primary bulk invasive cancers were analyzed. We report that CD44+/CD24-/low tumorigenic breast cancer cells were intrinsically chemoresistant â chemotherapy led to increased CD44+/CD24-/low cells, increased self-renewal capacity on MS assays, and enhanced tumorigeneicity in immunocompromised SCID/Beige mice. Conversely, in patients with HER2 overexpressing tumors, the EGFR/HER2 tyrosine kinase inhibitor, lapatinib decreased CD44+/CD24-/low cells, with the majority of these patients after conventional therapy achieving pathologic complete response, a validated surrogate marker for long-term survival. Gene transcription pathways that underlie chemoresistant, MS-forming CD44+/CD24-/low cells involve genes belonging to stem cell self-renewal, Wnt signaling, and early development pathways. Experiment Overall Design: Human breast tumor samples were sorted using flow cytometry to select for cells that were CD44+ and CD24-. Gene expression profiles of these cells were compared with profiles of the other sorted cells (CD24+ and CD44-/CD24-). Experiment Overall Design: Core biopsies of primary breast tumors were taken and placed immediately in cold RPMI-1640 supplemented with 10% heat-inactivated newborn calf serum (HINCS, Invitrogen, Carlsbad, CA). Within an hour, the samples were minced and then digested in 10-15 mL of MEGM with 250-300 units/mL collagenase at 370C. The samples were filtered, washed, and then subjected to hypotonic shock to lyse red blood cells. About 106 single cells were re-suspended, incubated for 15 min at 40C with anti-CD44 (APC), anti-CD24 (FITC), and anti-lineage cocktail antibodies (PE-conjugated anti-CD2, CD3, CD10, CD16, CD18, CD31 and CD 140B) (Pharmingen, San Diego, CA) using the manufacturerâs suggested concentrations. The cells were then washed twice, re-suspended with the viability dye propidium iodide, and analyzed using Dako MoFlo flow cytometry. Side- and forward- scatter were used to eliminate debris and cell doublets, and the Lin- cells were further analyzed by CD44 and CD24 markers.
2009-08-22 | E-GEOD-7513 | biostudies-arrayexpress