Label-Free High Dimensional Single Cell Morphological Profiling of Different Hematological Malignancies By Ghost Cytometry
Authors: Tsubouchi Asako , Miettinen Juho Jalmari , Wagatsuma Keisuke , Akai Satoru , An Yuri , Uematsu Mika , Sergeev Philipp , Tsallos Dimitrios , Vaha-Koskela Markus , Ota Sadao , Heckman Caroline A. 
Published: December 2023

Characterizing the heterogeneity in human leukemias is critical for understanding mechanisms of disease onset, uncovering biomarkers for disease diagnosis, and guiding research into novel treatment approaches. While extensive research focus has been placed on characterizing the molecular heterogeneity in leukemia by modern 'omics technologies, comparatively less emphasis has been placed on studying the diversity in holistic morphological changes that occur in single cells in the context of disease. Here, we report on a novel approach to understanding and characterizing different hematological malignancies using ghost cytometry, a recently developed high-content flow cytometric method that leverages high-speed, artificial intelligence (AI) driven morphological characterization and analysis of single cells.

We performed morphometric profiling on human bone marrow samples from patients with T and B-cell acute lymphoblastic leukemia (ALL, n=5), acute myeloid leukemia (AML, n=5), multiple myeloma (MM, n=5) and healthy bone marrow mononuclear cell (BM-MNC) controls (n=6) (Figure 1). Morphological profiles from individual cells were analyzed by unsupervised machine learning (universal manifold approximation and projection, UMAP) and compared within disease subsets and against controls. We identified disease-specific cell populations from a mixture of healthy control and disease samples without any labeling, based on completely label-free information (Figure 2). For validation of the specific cell subsets, we overlaid known cell surface markers (e.g. CD45 for blast cells, CD10/CD19 for B-ALL, CD33/CD34 for AML, CD38/CD138 for MM). The intensity of CD45 was dimmer and disease-specific markers were highly expressed in the disease-specific cell population. We also identified unique cell populations across blood cell populations that were distinct from the healthy BM-MNC controls. We also analyzed three human myeloma cell lines (AMO1, KMS, and L363) for comparison to the primary MM samples. In the UMAP analysis using LF-GC data, in all three types of cell lines, Bortezomib-resistant cell lines exhibited different distributions from the control parental cell lines. In addition, we observed distinct morphological profiles between primary myeloma samples and myeloma cell lines.

Here we present a novel cell characterization approach for human hematological diseases that leverages AI-based, label-free, high-speed morphological characterization of single cells. We demonstrate that the approach can be used to identify subtle morphological differences in blast cells from a range of blood cancer subtypes. Application of morphological profiling and AI can be used to identify measurable, disease-related changes in these diseases that have diagnostic, drug screening, and therapeutic monitoring potential.

Keywords: flow cytometry, acute leukemia, multiple myeloma, label-free cell analysis, biomarker, liquid biopsy, ghost cytometry, artificial intelligence, machine learning

Figure 1. Research design overview. Bone marrow samples from patients and non-diseased controls will be analyzed by Ghost Cytometry to assess disease by conventional markers and identify novel morphometric subpopulations. Morphological information is acquired as label-free GMI signals without any labeling. The label-free GMI information is transformed into two-dimensional space by UMAP dimensionality reduction method to identify subpopulations of interest. Both samples are stained with known cell surface markers for validation of unsupervised classification.

Figure 2. Identification of disease-specific cell subpopulations by unsupervised approach. Using an unsupervised machine learning technique called UMAP, label-free morphologically distinct cellular subpopulations can be identified in B-ALL, AML, MM when compared to healthy donor controls (left panels, inside the dotted circles). For validation of the subpopulations, known cell surface markers (CD45 for blast cells, CD10/CD19 for B-ALL, CD33/CD34 for AML, CD38/CD138 for MM) and cell trace marker (CellTracker) were overlaid. The intensity increases monotonically through the colormaps. The intensity of CD45 was dimmer and disease-specific markers were highly expressed in the disease-specific cell population.