Contributed Talk

Single-cell physical phenotyping of mechanically dissociated tissue biopsies for fast diagnostic assessment

Markéta Kubánková¹, Despina Soteriou¹, Christine Schweitzer¹, Rocío López-Posadas^2,4, Rashmita Pradhan^2,4, Oana-Maria Thoma^2,4,7, Andrea-Hermina Györfi^3,4, Alexandru-Emil Matei^3,4, Maximilian Waldner^2,4,7, Jörg H. W. Distler^3,4, Stefan Scheuermann⁵, Jens Langejürgen⁵, Regine Schneider-Stock^6,7, Raja Atreya^2,4,7, Markus F. Neurath^2,4,7, Arndt Hartmann^6,7, Jochen Guck¹

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Real-time deformability cytometry (RT-DC) [1] is a microfluidic technique that allows the assessment of physical properties of single cells in a label-free and high-throughput manner, with up to 1000 cells analysed per second. In addition to cell deformability, other physical phenotype parameters are obtained directly from cell images including the cell size, aspect ratio and solidity. The diagnostic potential of RT-DC has been demonstrated in many human diseases ranging from leukemia to bacterial and viral infections including COVID-19 [2,3]. Nevertheless, until now, the applicability of the technique was limited to analysing cultured cells or liquid biopsies from blood or bone marrow.

Here, we present a rapid label-free diagnostic method for solid tissue biopsies. The approach combines the enzyme-free, mechanical dissociation of tissues using a tissue grinder for the quick and simple isolation of viable single cells [4] with the sequential assessment of cellular physical phenotypes of thousands of individual cells using RT-DC, and machine learning-based analysis [5].

First, we screen a panel of different mouse tissues and assessed the cell yield, viability and the feasibility of RT-DC measurement upon mechanical dissociation. We show that the physical phenotype parameters extracted from brightfield images of single cells can be used to distinguish subpopulations of cells in various tissues, without prior knowledge or the need for molecular markers. We examine frozen and fresh biopsy samples from mouse and human colon and using unsupervised dimensionality reduction and logistic regression, we accurately differentiate between healthy and tumorous tissue.

The findings demonstrate that assessing the physical phenotype of tissue-derived single cells using RT-DC is a powerful strategy to detect a malignant state in tissue, laying groundwork for a new diagnostic tool. We envision it as an alternative to the prevalent method of intraoperative consultation pathology which is time, labour and cost intensive and requires the expertise of trained pathologists. Our procedure, which delivers results within 30 minutes, has potential as a fast and marker-free diagnostic pipeline to sensitively detect pathological changes in biopsies. More generally, it is a powerful research tool for identifying and characterizing cell populations in tissues in an unbiased and marker-free manner.