Lymphocyte Classification from Hoechst Stained Slides with Deep Learning

Abstract

Multiplex immunofluorescence and immunohistochemistry benefit patients by allowing cancer pathologists to identify proteins expressed on the surface of cells. This enables cell classification, better understanding of the tumour microenvironment, and more accurate diagnoses, prognoses, and tailored immunotherapy based on the immune status of individual patients. However, these techniques are expensive. They are time consuming processes which require complex staining and imaging techniques by expert technicians. Hoechst staining is far cheaper and easier to perform, but is not typically used as it binds to DNA rather than to the proteins targeted by immunofluorescence techniques. In this work we show that through the use of deep learning it is possible to identify an immune cell subtype without immunofluorescence. We train a deep convolutional neural network to identify cells expressing the T lymphocyte marker CD3 from Hoechst 33342 stained tissue only. CD3 expressing cells are often used in key prognostic metrics such as assessment of immune cell infiltration, and by identifying them without the need for costly immunofluorescence, we present a promising new approach to cheaper prediction and improvement of patient outcomes. We also show that by using deep learning interpretability techniques, we can gain insight into the previously unknown morphological features which make this possible.

Whole-Slide Images and Patches of Clear Cell Renal Cell Carcinoma Tissue Sections Counterstained with Hoechst 33342, CD3, and CD8 Using Multiple Immunofluorescence

“…These advancements, combined with the availability of digital pathology images, pave the way towards developing automated methods for WSI analysis. Potential applications vary from slide-level tasks such as patient risk stratification [13,14], to specific image tasks such as detecting cellular subtypes and their spatial distribution [15][16][17]. In this setting, deep learning not only has the potential to help reduce the workload of pathologists, but also to alleviate inter-observer bias, which is a common problem in pathology [18,19].…”

The Anti-Cancer Effect of Cinnamon Aqueous Extract: A Focus on Hematological Malignancies

“…In vitro studies [ 62 ] show that cinnamon extract is capable of inhibiting the growth of HL-60 cells, which are promyeloblasts isolated from the peripheral blood of patients with APL and used in scientific research. The easiest method to investigate cell apoptosis consists of the use of Hoechst staining, which puts in evidence differences in nuclear chromatin and, consequently, lets us identify healthy and apoptotic cells through the morphology of the cell nucleus [ 84 , 85 , 86 , 87 , 88 ]. In particular, apoptotic cells appear like small bodies with fragmented and peripheral deoxyribonucleic acid (DNA), while healthy cells are larger and oval with a single and undefined nucleus.…”