# High-Efficiency Enrichment of Megakaryocytes and Identification of Micromegakaryocytes from Human Bone Marrow by Imaging Flow Cytometry

## TL;DR

This paper introduces a new method to efficiently identify and enrich megakaryocytes in human bone marrow using imaging flow cytometry and machine learning, improving diagnostic accuracy.

## Contribution

A novel approach combining CD41 enrichment, immunophenotyping, and a convolutional neural network to enhance megakaryocyte detection and reduce false positives.

## Key findings

- CD41 enrichment increased megakaryocyte frequency nearly 200-fold.
- A convolutional neural network reduced false positives by 94.9%.
- The method enables accurate detection of both mature and immature megakaryocytes.

## Abstract

Megakaryocytes (MKs) are rare, large, polyploid bone marrow (BM) cells responsible for the production of platelets. The identification and characterization of MKs is widely recognized as challenging. Manual microscopy is especially difficult due to the rarity and complex morphology of MKs, while flow cytometry faces additional challenges from MKs’ large size, fragility, and platelet adhesion, causing false positives. We present a novel approach to accurately enrich MKs from human BM aspirates with a specific focus on the detection and quantification of microMKs. By integrating CD41+ cell enrichment, immunophenotyping, and morphometric analysis, we identified cells of the megakaryocytic lineage. To increase accuracy, a convolutional neural network was trained to identify CD41− cells falsely displaying an MK-like immunophenotype due to adhesive CD41+ platelets. This allowed for exclusion of 94.9% of false positive events, considerably enhancing specificity. CD41 positive enrichment prior to imaging flow cytometry acquisition increased the MK frequency nearly 200-fold, yielding a population of both mature and immature MKs, thus supporting analysis of MK progenitors. Overall, this advanced approach enables enrichment of MKs from human BM, considerably increasing the accuracy and statistical power of the MK analysis. This may provide an important addition in the context of MK-related diagnostics and research.