Acute lymphoblastic leukemia (ALL) is the most common childhood cancer. While there are a number of well‐recognized prognostic biomarkers at diagnosis, the most powerful independent prognostic factor is the response of the leukemia to induction chemotherapy (Campana and Pui: Blood 129 (2017) 1913–1918). Given the potential for machine learning to improve precision medicine, we tested its capacity to monitor disease in children undergoing ALL treatment. Diagnostic and on‐treatment bone marrow samples were labeled with an ALL‐discriminating antibody combination and analyzed by imaging flow cytometry. Ignoring the fluorescent markers and using only features extracted from bright‐field and dark‐field cell images, a deep learning model was able to identify ALL cells at an accuracy of >88%. This antibody‐free, single cell method is cheap, quick, and could be adapted to a simple, laser‐free cytometer to allow automated, point‐of‐care testing to detect slow early responders. Adaptation to other types of leukemia is feasible, which would revolutionize residual disease monitoring. © 2020 The Authors.
Megakaryocytes are a rare population of cells that develop in the bone marrow and function to produce platelets that circulate throughout the body and form clots to stop or prevent bleeding. A major challenge in studying megakaryocyte development, and the diseases that arise from their dysfunction, is the identification, classification, and enrichment of megakaryocyte progenitor cells that are produced during hematopoiesis. Here, we present a high throughput strategy for identifying and isolating megakaryocytes and their progenitor cells from a heterogeneous population of bone marrow samples. Specifically, we couple thrombopoietin (TPO) induction, image flow cytometry, and principal component analysis (PCA) to identify and enrich for megakaryocyte progenitor cells that are capable of self-renewal and directly differentiating into mature megakaryocytes. This enrichment strategy distinguishes megakaryocyte progenitors from other lineage-committed cells in a high throughput manner. Furthermore, by using image flow cytometry with PCA, we have identified a combination of markers and characteristics that can be used to isolate megakaryocyte progenitor cells using standard flow cytometry methods. Altogether, these techniques enable the high throughput enrichment and isolation of cells in the megakaryocyte lineage and have the potential to enable rapid disease identification and diagnoses ahead of severe disease progression.
more » « less- NSF-PAR ID:
- 10221990
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- Scientific Reports
- Volume:
- 11
- Issue:
- 1
- ISSN:
- 2045-2322
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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