Image-enabled cell sorting for cellular drug response detection: A machine learning platform for indication discovery

This article is based on a poster originally authored by Barbie Wang, Maria Giebler, Adrian Freeman, Karen Hogg, Adam Corrigan and Hitesh Sanganee.

This poster is being hosted on this website in its raw form, without modifications. It has not undergone peer review but has been reviewed to meet AZoNetwork's editorial quality standards. The information contained is for informational purposes only and should not be considered validated by independent peer assessment.

Abstract

Imaging flow cytometry (IFC) enables the identification of cells with the throughput of a traditional flow cytometer, with added imaging features enabling single cell image acquisitions and cell sorting based on phenotypes. We collaborated with the imaging facility at the University of York to access BD FACSDiscover S8 imaging flow cytometry system to explore the potential of Phenospace as an AI tool to determine patient cellular phenotypes.

The generation of cellular images and transcriptomic data from respective sorted cell populations allows us to train a machine learning platform to exclusively identify transcriptomic changes within cells based solely on the analysis of images. This work establishes crucial groundwork, yielding multiple proof of concept data for advancing this methodology toward clinical applications.

Introduction

• IFC allows for real-time visualization of cell-cell interaction, co-localization, and cell phenotypic changes. We generate images from drug-treated primary immune cells and underpin the transcriptomic changes using NGS of sorted populations.
• To mimic disease cell states, we used IFNa as a stimulant to induce a phenotype known from systemic Lupus Erythematous (SLE), while Saphnelo (INFAR-inhibitor) was used to restore cells to a healthy phenotype. Leveraging our phenotypic data, we fed it into an in-house machine learning image analysis tool, Phenospace, to generate imaging profiles that could be linked to respective transcriptomic profiles.

Methods

Image Credit: Image courtesy of Barbie Wang et al., in partnership with ELRIG (UK) Ltd.

Results

A: Gating strategies allow for identification of distinct cell types based on flow parameters

Image Credit: Image courtesy of Barbie Wang et al., in partnership with ELRIG (UK) Ltd.

In (V), the relevant surface expression markers were analysed to determine the effect of IFNa and Anifrolumabs on different cell types. Positive CD3 expression allows for selection of T cells, CD19 for B-cells while CD14 and CD11b are used as inflammatory markers.

B. Analysis of expression and DEGs

A) Bar graphs created using GraphPad, showcasing expression of CD11b and CD14 in PBMCs upon stimulation with IFNa and Anifrolumab. B) Comparison between the number of upregulated vs downregulated genes under different treatment conditions. DEGs were generated from sorted live cells as seen in gating strategies (III). Samples treated with Anifrolumab can be seen to have minimal DEGs, evidencing success in returning cells to healthy state. C) KEGG showing the least to most significant pathways extracted from top 500 DEGs from 150 ng IFNa treated samples compared to control samples. Image Credit: Image courtesy of Barbie Wang et al., in partnership with ELRIG (UK) Ltd.

Future opportunities

Phenospace, an in-house ML image analyzing tool differentiates between the different cellular phenotypes, allowing clustering of the same cell types based on images. The next step is to evaluate whether it can distinguish between different cell states. QC of Phenospace predictions can be done through the generation of confusion matrices.

Image Credit: Image courtesy of Barbie Wang et al., in partnership with ELRIG (UK) Ltd.

Conclusion and next steps

• Generated multiple proof-of-concept data, including images and transcriptomic data.
• Train Phenospace using this batch of data to see if it can distinguish between stimulated and unstimulated cell types based on images.
• Utilize artificial intelligence (AI) to aid in the discovery of new compound indications and to distinguish responding from nonresponding patient samples.

Acknowledgments

Maria Giebler, Adrian Freeman, Agnieszka Jozwik, Karen Hogg, Adam Corrigan, Sukhveer Mann, Wider EIU and FGx team.

About AstraZeneca

AstraZeneca is a global, science-led patient-focused pharmaceutical company that focuses on transforming the future of healthcare by unlocking the power of what science can do for people, society and the planet.

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