Publications

Alan M. Race, et al, Analytical Chemistry, 2021
With increasing demand to correlate data across multiple imaging modalities, Race and colleagues demonstrate a mechanism by which annotations can be generated on images from one imaging modality and transferred to a second image modality for data integration (and optionally, back again). Further, they perform this workflow on mass spectrometry images of a pancreatic cancer mouse model and hematoxylin and eosin-stained sections. HALO and HALO AI image analysis software was used to develop a DenseNet algorithm to classify and generate annotations for pancreatic ductal carcinoma tumor, non-neoplastic acinar tissue, and connective tissue on H&E-stained slides. This method for bidirectional transfer of image annotations may enable novel workflows in the future.

Juliana de Almeida-Faira, et al, Diabetologia, 2021
In this study, researchers set out to understand how miR-126-3, a microRNA found at increased levels in offspring of maternally obese mice, functioned in adipocyte metabolism. de Almeida-Faria and colleagues used proteomic approaches to identify a novel ER protein that is a direct target of miR-126-3 called Lunapark. HALO and HALO AI were used to train a DenseNet algorithm to selectively identify crown-like structures in H&E-stained fat tissue. Further, de Almeida-Faria and colleagues demonstrate that maternal obesity in mice leads to an increased risk of type 2 diabetes in offspring by targeting miR-126-3 regulation. Therefore, miR-126-3 is identified as a potential therapeutic target that could impact obesity and type 2 diabetes.

Liang Guo, et al, Clinical Science, 2020
Prior to this publication it was known that dysfunctional PD-1hi CD8+ T cells infiltrated tumors, although it was unknown if this phenotype played a role in triple-negative breast cancer (TNBC). Guo et al set out to explore this phenotype in triple-negative breast cancer and using HALO and HALO AI demonstrated using both quantitative multiplexed immunohistochemistry and multispectral fluorescence imaging that PD-1hi CD8+ T cells were found in TNBC patient tissue biopsy core analysis but largely absent from peripheral blood. Molecular analysis of these cells revealed expression of biomarkers associated with T-cell exhaustion and the authors hypothesize that this cellular phenotype could be useful for future stratification and as a prognostic marker in TNBC patients as the presence of PD-1hi CD8+ T cells are associated with favorable outcomes. In addition, future research with this cellular phenotype may provide opportunity for therapeutic advancement in TNBC, a challenging subtype of breast cancer to treat.

Jordan Redemann, et al, Journal of Pathology Informatics, 2020
Metastatic neuroendocrine tumors behave differently according to site of origin and it is important clinically to identify the primary site in order to identify an appropriate therapy. The site of origin in neuroendocrine tumors are challenging to identify based on H&E alone and can require an immunohistochemistry (IHC) panel. Redemann and colleagues evaluated the performance of HALO AI, a deep-learning convolutional neural network (CNN) on site of origin identification from a set of metastatic well-differentiated neuroendocrine tumors with known sites of origin and compared against IHC slides scored by pathologists. HALO AI was trained with H&E-stained tissue microarrays and was then evaluated against IHC analysis to identify pancreas/duodenum, ileum/jejunum/duodenum, colorectum/appendix, and lung. Results showed that HALO AI correctly identified the site of origin in 70% of cases and IHC correctly identified 76% of cases. As this was statistically insignificant, the authors conclude that a trained CNN can identify a site of origin from a well differentiated neuroendocrine tumor using morphology data alone with accuracy similar to that of IHC, the clinical gold standard.