Hoechst Is All You Need: Lymphocyte Classification with Deep Learning

TL;DR

This paper demonstrates that a deep learning model can accurately classify lymphocyte subtypes using only Hoechst DNA stain images, eliminating the need for expensive multiplex immunofluorescence techniques.

Contribution

The study introduces a novel deep learning approach that identifies lymphocyte protein expression solely from Hoechst-stained images, revealing previously unknown morphological features.

Findings

Achieved over 90% precision and recall in cell classification

Identified new morphological features linked to protein expression

Reduced reliance on costly immunofluorescence techniques

Abstract

Multiplex immunofluorescence and immunohistochemistry benefit patients by allowing cancer pathologists to identify several proteins expressed on the surface of cells, enabling cell classification, better understanding of the tumour micro-environment, more accurate diagnoses, prognoses, and tailored immunotherapy based on the immune status of individual patients. However, they are expensive and time consuming processes which require complex staining and imaging techniques by expert technicians. Hoechst staining is much cheaper and easier to perform, but is not typically used in this case as it binds to DNA rather than to the proteins targeted by immunofluorescent techniques, and it was not previously thought possible to differentiate cells expressing these proteins based only on DNA morphology. In this work we show otherwise, training a deep convolutional neural network to identify cells expressing three proteins (T lymphocyte markers CD3 and CD8, and the B lymphocyte marker CD20) with greater than 90% precision and recall, from Hoechst 33342 stained tissue only. Our model learns previously unknown morphological features associated with expression of these proteins which can be used to accurately differentiate lymphocyte subtypes for use in key prognostic metrics such as assessment of immune cell infiltration,and thereby predict and improve patient outcomes without the need for costly multiplex immunofluorescence.