Virtual histological staining of unlabeled autopsy tissue

Histological examination is a crucial step in an autopsy; however, the traditional histochemical staining of post-mortem samples faces multiple challenges, including the inferior staining quality due to autolysis caused by delayed fixation of cadaver tissue, as well as the resource-intensive nature of chemical staining procedures covering large tissue areas, which demand substantial labor, cost, and time. These challenges can become more pronounced during global health crises when the availability of histopathology services is limited, resulting in further delays in tissue fixation and more severe staining artifacts. Here, we report the first demonstration of virtual staining of autopsy tissue and show that a trained neural network can rapidly transform autofluorescence images of label-free autopsy tissue sections into brightfield equivalent images that match hematoxylin and eosin (H&E) stained versions of the same samples, eliminating autolysis-induced severe staining artifacts inherent in traditional histochemical staining of autopsied tissue. Our virtual H&E model was trained using >0.7 TB of image data and a data-efficient collaboration scheme that integrates the virtual staining network with an image registration network. The trained model effectively accentuated nuclear, cytoplasmic and extracellular features in new autopsy tissue samples that experienced severe autolysis, such as COVID-19 samples never seen before, where the traditional histochemical staining failed to provide consistent staining quality. This virtual autopsy staining technique can also be extended to necrotic tissue, and can rapidly and cost-effectively generate artifact-free H&E stains despite severe autolysis and cell death, also reducing labor, cost and infrastructure requirements associated with the standard histochemical staining.Comment: 24 Pages, 7 Figure

Trends in Octogenarian Pathology

The global population has been gradually aging over the past few decades, with a growing proportion of people aged 65 years or older. Simultaneously, the medical profession has shifted towards promoting the concept of “positive” gerontology, emphasizing healthy aging. In this context, we investigated the trends in pathological specimen submissions from patients aged 85 or older over ten years at a single center. We observed a nearly two-fold increase in submissions during 2015–2019 compared to 2010–2014, out of proportion to the change in the fraction of older adults in the population, suggesting a changing attitude towards medical care in these older patients. Dermatologic samples were the main driver of growth, followed by gastrointestinal and urinary tract samples. However, other samples, like breast and lung, did not significantly increase. Although further research is needed to understand the implications of increasing invasive procedures in the oldest old, a noteworthy trend has emerged towards increased and more active healthcare for this population. Healthcare providers and administrators should be prepared for a continued rise in invasive interventions in this age group

From genes to modules, from cells to ecosystems

Twenty years ago, molecular biology transitioned from predominantly studying genes as isolated elements to viewing them as part of complex modules, giving rise to the field of systems biology. This transition was made possible by technological advances that allowed to simultaneously measure the expression levels of thousands of genes in a single experiment and drove a shift toward analyses identifying gene sets, modules, and pathways involved in a biological process of interest. Today we are excitingly facing a similar turning point in cell biology, where single‐cell technologies have enabled us to approach cells as cellular modules

Tumor‐infiltrating lymphocyte transfusion in a patient with treatment refractory triple negative breast cancer

Abstract Background Triple negative breast cancer (TNBC) is an aggressive form of breast cancer that is treated with chemotherapy. Recently, programmed death 1 (PD1) inhibition, as well as antibody‐drug conjugates, have been added to the available treatment regimen, yet metastatic disease is fatal. Adoptive cell therapy (ACT) using tumor infiltrating lymphocytes (TILs) has been well described in melanoma, but less data is available on other solid malignancies. Case Herein, we present a case of a 31‐year‐old patient diagnosed with Breast Cancer gene 1 (BRCA1) positive, TNBC. The patient's disease rapidly progressed while under standard treatment protocols. As a result, additional genetic testing of the tumor was carried out and revealed loss of BRCA1 heterozygosity, a double Tumor Protein 53 (TP53) mutation, and MYC amplification. Due to resistance to conventional therapy, an experimental approach was attempted using tumor‐infiltrating lymphocytes in November 2021 at Hadassah University Medical Center. While receiving this treatment, the patient exhibited a reported subjective clinical improvement including a month spent out of the hospital. However, the final result, presumably due to Interleukin 2 (IL‐2) toxicity, was the patient's passing. Conclusion This case is unique and peculiar regarding the treatment modality chosen, due to the extremely refractory disease the patient suffered from. After standard therapies rapidly failed, adoptive cell therapy was attempted with the infusion of TILs. This treatment has been shown effective in melanoma, however, there is an extreme paucity of data on other solid tumors, including TNBC. Although the patient ultimately demised presumably due to treatment side effects, brief clinical benefit was apparent. Further studies are warranted

Virtual histological staining of unlabeled autopsy tissue.

Traditional histochemical staining of post-mortem samples often confronts inferior staining quality due to autolysis caused by delayed fixation of cadaver tissue, and such chemical staining procedures covering large tissue areas demand substantial labor, cost and time. Here, we demonstrate virtual staining of autopsy tissue using a trained neural network to rapidly transform autofluorescence images of label-free autopsy tissue sections into brightfield equivalent images, matching hematoxylin and eosin (H&E) stained versions of the same samples. The trained model can effectively accentuate nuclear, cytoplasmic and extracellular features in new autopsy tissue samples that experienced severe autolysis, such as COVID-19 samples never seen before, where the traditional histochemical staining fails to provide consistent staining quality. This virtual autopsy staining technique provides a rapid and resource-efficient solution to generate artifact-free H&E stains despite severe autolysis and cell death, also reducing labor, cost and infrastructure requirements associated with the standard histochemical staining

Virtual histological staining of unlabeled autopsy tissue

Abstract Traditional histochemical staining of post-mortem samples often confronts inferior staining quality due to autolysis caused by delayed fixation of cadaver tissue, and such chemical staining procedures covering large tissue areas demand substantial labor, cost and time. Here, we demonstrate virtual staining of autopsy tissue using a trained neural network to rapidly transform autofluorescence images of label-free autopsy tissue sections into brightfield equivalent images, matching hematoxylin and eosin (H&E) stained versions of the same samples. The trained model can effectively accentuate nuclear, cytoplasmic and extracellular features in new autopsy tissue samples that experienced severe autolysis, such as COVID-19 samples never seen before, where the traditional histochemical staining fails to provide consistent staining quality. This virtual autopsy staining technique provides a rapid and resource-efficient solution to generate artifact-free H&E stains despite severe autolysis and cell death, also reducing labor, cost and infrastructure requirements associated with the standard histochemical staining