Unstained Tissue Imaging and Virtual Hematoxylin and Eosin Staining of Histologic Whole Slide Images

Tissue structures, phenotypes, and pathology are routinely investigated based on histology. This includes chemically staining the transparent tissue sections to make them visible to the human eye. Although chemical staining is fast and routine, it permanently alters the tissue and often consumes hazardous reagents. On the other hand, on using adjacent tissue sections for combined measurements, the cell-wise resolution is lost owing to sections representing different parts of the tissue. Hence, techniques providing visual information of the basic tissue structure enabling additional measurements from the exact same tissue section are required. Here we tested unstained tissue imaging for the development of computational hematoxylin and eosin (HE) staining. We used unsupervised deep learning (CycleGAN) and whole slide images of prostate tissue sections to compare the performance of imaging tissue in paraffin, as deparaffinized in air, and as deparaffinized in mounting medium with section thicknesses varying between 3 and 20 μm. We showed that although thicker sections increase the information content of tissue structures in the images, thinner sections generally perform better in providing information that can be reproduced in virtual staining. According to our results, tissue imaged in paraffin and as deparaffinized provides a good overall representation of the tissue for virtually HE-stained images. Further, using a pix2pix model, we showed that the reproduction of overall tissue histology can be clearly improved with image-to-image translation using supervised learning and pixel-wise ground truth. We also showed that virtual HE staining can be used for various tissues and used with both 20× and 40× imaging magnifications. Although the performance and methods of virtual staining need further development, our study provides evidence of the feasibility of whole slide unstained microscopy as a fast, cheap, and feasible approach to producing virtual staining of tissue histology while sparing the exact same tissue section ready for subsequent utilization with follow-up methods at single-cell resolution.publishedVersionPeer reviewe

Deformation equivariant cross-modality image synthesis with paired non-aligned training data

Cross-modality image synthesis is an active research topic with multiple medical clinically relevant applications. Recently, methods allowing training with paired but misaligned data have started to emerge. However, no robust and well-performing methods applicable to a wide range of real world data sets exist. In this work, we propose a generic solution to the problem of cross-modality image synthesis with paired but non-aligned data by introducing new deformation equivariance encouraging loss functions. The method consists of joint training of an image synthesis network together with separate registration networks and allows adversarial training conditioned on the input even with misaligned data. The work lowers the bar for new clinical applications by allowing effortless training of cross-modality image synthesis networks for more difficult data sets

The effect of neural network architecture on virtual H&E staining : Systematic assessment of histological feasibility

Conventional histopathology has relied on chemical staining for over a century. The staining process makes tissue sections visible to the human eye through a tedious and labor-intensive procedure that alters the tissue irreversibly, preventing repeated use of the sample. Deep learning-based virtual staining can potentially alleviate these shortcomings. Here, we used standard brightfield microscopy on unstained tissue sections and studied the impact of increased network capacity on the resulting virtually stained H&E images. Using the generative adversarial neural network model pix2pix as a baseline, we observed that replacing simple convolutions with dense convolution units increased the structural similarity score, peak signal-to-noise ratio, and nuclei reproduction accuracy. We also demonstrated highly accurate reproduction of histology, especially with increased network capacity, and demonstrated applicability to several tissues. We show that network architecture optimization can improve the image translation accuracy of virtual H&E staining, highlighting the potential of virtual staining in streamlining histopathological analysis.publishedVersionPeer reviewe

miR-32 promotes MYC-driven prostate cancer

miR-32 is an androgen receptor (AR)-regulated microRNA, expression of which is increased in castration-resistant prostate cancer (PC). We have previously shown that overexpression of miR-32 in the prostate of transgenic mice potentiates proliferation in prostate epithelium. Here, we set out to determine whether increased expression of miR-32 influences growth or phenotype in prostate adenocarcinoma in vivo. We studied transgenic mice expressing MYC oncogene (hiMYC mice) to induce tumorigenesis in the mouse prostate and discovered that transgenic overexpression of miR-32 resulted in increased tumor burden as well as a more aggressive tumor phenotype in this model. Elevated expression of miR-32 increased proliferation as assessed by Ki-67 immunohistochemistry, increased nuclear density, and higher mitotic index in the tumors. By gene expression analysis of the tumorous prostate tissue, we confirmed earlier findings that miR-32 expression regulates prostate secretome by modulating expression levels of several PC-related target genes such as Spink1, Spink5, and Msmb. Further, we identified Pdk4 as a tumor-associated miR-32 target in the mouse prostate. Expression analysis of PDK4 in human PC reveals an inverse correlation with miR-32 expression and Gleason score, a decrease in castration-resistant and metastatic tumors compared to untreated primary PC, and an association of low PDK4 expression with a shorter recurrence-free survival of patients. Although decreased PDK4 expression induces the higher metabolic activity of PC cells, induced expression of PDK4 reduces both mitotic respiration and glycolysis rates as well as inhibits cell growth. In conclusion, we show that miR-32 promotes MYC-induced prostate adenocarcinoma and identifies PDK4 as a PC-relevant metabolic target of miR-32-3p.publishedVersionPeer reviewe

miR-32 promotes MYC-driven prostate cancer

miR-32 is an androgen receptor (AR)-regulated microRNA, expression of which is increased in castration-resistant prostate cancer (PC). We have previously shown that overexpression of miR-32 in the prostate of transgenic mice potentiates proliferation in prostate epithelium. Here, we set out to determine whether increased expression of miR-32 influences growth or phenotype in prostate adenocarcinoma in vivo. We studied transgenic mice expressing MYC oncogene (hiMYC mice) to induce tumorigenesis in the mouse prostate and discovered that transgenic overexpression of miR-32 resulted in increased tumor burden as well as a more aggressive tumor phenotype in this model. Elevated expression of miR-32 increased proliferation as assessed by Ki-67 immunohistochemistry, increased nuclear density, and higher mitotic index in the tumors. By gene expression analysis of the tumorous prostate tissue, we confirmed earlier findings that miR-32 expression regulates prostate secretome by modulating expression levels of several PC-related target genes such as Spink1, Spink5, and Msmb. Further, we identified Pdk4 as a tumor-associated miR-32 target in the mouse prostate. Expression analysis of PDK4 in human PC reveals an inverse correlation with miR-32 expression and Gleason score, a decrease in castration-resistant and metastatic tumors compared to untreated primary PC, and an association of low PDK4 expression with a shorter recurrence-free survival of patients. Although decreased PDK4 expression induces the higher metabolic activity of PC cells, induced expression of PDK4 reduces both mitotic respiration and glycolysis rates as well as inhibits cell growth. In conclusion, we show that miR-32 promotes MYC-induced prostate adenocarcinoma and identifies PDK4 as a PC-relevant metabolic target of miR-32-3p.</p

ACROBAT -- a multi-stain breast cancer histological whole-slide-image data set from routine diagnostics for computational pathology

The analysis of FFPE tissue sections stained with haematoxylin and eosin (H&E) or immunohistochemistry (IHC) is an essential part of the pathologic assessment of surgically resected breast cancer specimens. IHC staining has been broadly adopted into diagnostic guidelines and routine workflows to manually assess status and scoring of several established biomarkers, including ER, PGR, HER2 and KI67. However, this is a task that can also be facilitated by computational pathology image analysis methods. The research in computational pathology has recently made numerous substantial advances, often based on publicly available whole slide image (WSI) data sets. However, the field is still considerably limited by the sparsity of public data sets. In particular, there are no large, high quality publicly available data sets with WSIs of matching IHC and H&E-stained tissue sections. Here, we publish the currently largest publicly available data set of WSIs of tissue sections from surgical resection specimens from female primary breast cancer patients with matched WSIs of corresponding H&E and IHC-stained tissue, consisting of 4,212 WSIs from 1,153 patients. The primary purpose of the data set was to facilitate the ACROBAT WSI registration challenge, aiming at accurately aligning H&E and IHC images. For research in the area of image registration, automatic quantitative feedback on registration algorithm performance remains available through the ACROBAT challenge website, based on more than 37,000 manually annotated landmark pairs from 13 annotators. Beyond registration, this data set has the potential to enable many different avenues of computational pathology research, including stain-guided learning, virtual staining, unsupervised pre-training, artefact detection and stain-independent models

Chemical interrogation of nuclear size identifies compounds with cancer cell line specific effects on migration and invasion

[Image: see text] Background: Lower survival rates for many cancer types correlate with changes in nuclear size/scaling in a tumor-type/tissue-specific manner. Hypothesizing that such changes might confer an advantage to tumor cells, we aimed at the identification of commercially available compounds to guide further mechanistic studies. We therefore screened for Food and Drug Administration (FDA)/European Medicines Agency (EMA)-approved compounds that reverse the direction of characteristic tumor nuclear size changes in PC3, HCT116, and H1299 cell lines reflecting, respectively, prostate adenocarcinoma, colonic adenocarcinoma, and small-cell squamous lung cancer. Results: We found distinct, largely nonoverlapping sets of compounds that rectify nuclear size changes for each tumor cell line. Several classes of compounds including, e.g., serotonin uptake inhibitors, cyclo-oxygenase inhibitors, β-adrenergic receptor agonists, and Na(+)/K(+) ATPase inhibitors, displayed coherent nuclear size phenotypes focused on a particular cell line or across cell lines and treatment conditions. Several compounds from classes far afield from current chemotherapy regimens were also identified. Seven nuclear size-rectifying compounds selected for further investigation all inhibited cell migration and/or invasion. Conclusions: Our study provides (a) proof of concept that nuclear size might be a valuable target to reduce cell migration/invasion in cancer treatment and (b) the most thorough collection of tool compounds to date reversing nuclear size changes specific to individual cancer-type cell lines. Although these compounds still need to be tested in primary cancer cells, the cell line-specific nuclear size and migration/invasion responses to particular drug classes suggest that cancer type-specific nuclear size rectifiers may help reduce metastatic spread

The ACROBAT 2022 Challenge: Automatic Registration Of Breast Cancer Tissue

The alignment of tissue between histopathological whole-slide-images (WSI) is crucial for research and clinical applications. Advances in computing, deep learning, and availability of large WSI datasets have revolutionised WSI analysis. Therefore, the current state-of-the-art in WSI registration is unclear. To address this, we conducted the ACROBAT challenge, based on the largest WSI registration dataset to date, including 4,212 WSIs from 1,152 breast cancer patients. The challenge objective was to align WSIs of tissue that was stained with routine diagnostic immunohistochemistry to its H&E-stained counterpart. We compare the performance of eight WSI registration algorithms, including an investigation of the impact of different WSI properties and clinical covariates. We find that conceptually distinct WSI registration methods can lead to highly accurate registration performances and identify covariates that impact performances across methods. These results establish the current state-of-the-art in WSI registration and guide researchers in selecting and developing methods

Assessing goblet cell metaplasia and expression of peptidase inhibitor 15 in mouse prostate tissue

Prostate cancer is the second most common cancer and leading cause of cancer death in men. It is caused by malignant growth of the prostate gland. The largest risk factor is age, but it has one of the lowest fatality rates among cancers and most patients die of other causes. Prostate cancer is usually diagnosed together with high prostate specific antigen level. It can be treated with radio- or hormonal therapy depending how far the cancer has progressed. However, sometimes the cancer progresses to a stage where it is resistant to the treatment and will eventually lead to death. Histochemistry is the study of chemical components in tissues. In this thesis, the objective was to use three types of stainings to study goblet cell metaplasia and the expression of peptidase inhibitor 15 (PI15) in mouse prostate tissue. The purpose was to assess goblet cell metaplasia in aged and miR-32 transgenic mouse prostate tissue and to establish a protocol for staining of PI15. Haematoxylin-eosin staining was used to find areas of interest, which were then further stained with periodic acid-Schiff (PAS) staining to detect goblet cells by the mucin they secrete. For staining of peptidase inhibitor 15, an immunohistochemical protocol was established. PI15 is a gene in a family of secretory proteins expressed in some glandular structures, such as the prostate. In the tissues studied here, goblet cell metaplasia was found by the mucin inside the goblet cells and some by the mucin they secrete to the lumen inside the gland. The metaplasia could also be seen in the epithelial cells, which were taller than average. The metaplasia was located in the ventral and dorsal lobes of the tissue, close to the urethra, and often in several glands rather than just one, which indicated that the metaplasia had spread. No difference was found in the metaplasia between transgenic and wild type mouse prostate tissues. To further study the metaplasia, more tissue samples should be examined to see where exactly it is located, as well as to study the metaplasia in human prostate tissue. PI15 was expressed in the tissues and no large difference was found between the two fixatives tested. PAXgene fixation did show slightly better results in the whole tissue samples, which could be caused by the cross-linking formalin fixation causes. In addition, two different buffers were tested to optimise antigen retrieval. The buffers showed a larger difference and PI15 was better visible in the pH 9 buffer. In the future, also transgenic mouse prostate and human prostate should be tested with staining of PI15 to see whether PI15 has a role in prostate cancer development.Eturauhassyöpä on miesten toiseksi yleisin syöpä ja aiheuttaa eniten syöpäkuolemia miehillä. Syöpä johtuu eturauhasen pahanlaatuisesta kasvusta ja vanhemmilla miehillä on iän takia suurempi riski sairastua eturauhassyöpään. Eturauhassyövällä on kuitenkin yksi alhaisimmista kuolleisuuksista ja suurin osa sairastuneista kuolee muiden syiden takia. Eturauhassyöpä diagnosoidaan usein prostataspesifisen antigeenin korkealla arvolla. Sitä voidaan hoitaa säde- tai hormonihoidolla riippuen siitä, kuinka pitkälle syöpä on edennyt. Osa syövistä etenee kuitenkin vaiheeseen, jossa syöpä jatkaa leviämistä hoidoista huolimatta johtaen lopulla kuolemaan. Histokemia on kudosten kemiallisten komponenttien oppi. Opinnäytetyön tavoitteena oli kolmea värjäystekniikkaa apuna käyttäen tutkia pikarisolujen metaplasiaa ja peptidaasi-inhibiittori 15 (PI15) ekspressiota hiiren eturauhaskudoksessa. Tarkoituksena oli arvioida pikarisolujen metaplasiaa vanhoissa ja miR-32-transgeenisissä hiiren eturauhaskudoksissa ja kehittää värjäysprotokolla PI15:lle. Hematoksyliini-eosiini-värjäystä käytettiin kiinnostavien kudosten löytämiseen, joita sitten värjättiin perjodihappo-Schiff-värjäyksellä pikarisolujen löytämiseksi niiden erittämän musiinin avulla. Peptidaasi-inhibiittori 15:n värjäykseen kehitettiin immunohistokemiallinen värjäysmenetelmä. PI15 on geeni sekretoristen proteiinien perheessä, joka ekspressoituu rauhasrakenteissa, esimerkiksi eturauhasessa. Tutkituissa kudoksissa pikarisolumetaplasia löydettiin pikarisolujen erittämän musiinin avulla, joko itse solujen sisältä tai rauhasonteloista. Metaplasia voitiin myös havaita epiteelisoluissa, jotka olivat korkeampia kuin normaalisti. Metaplasia sijaitsi kudoksen ventraali- ja dorsaalilohkoissa, virtsaputken lähellä, ja usein useammassa kuin yhdessä rauhasessa. Tämä osoitti metaplasian levinneen. Transgeenisten ja villityyppien välillä metaplasiassa ei huomattu eroavaisuutta. Jatkossa metaplasiaa voitaisiin tutkia useammissa kudosnäytteissä, jotta voitaisiin selvittää, missä se tarkalleen sijaitsee, sekä tutkia metaplasiaa ihmisen eturauhaskudoksessa. PI15 oli ekspressoitu kudoksissa ja suurta eroavaisuutta ei löydetty kahden testatun fiksatiivin välillä. PAXgene-fiksaatiolla saatiin hieman parempia tuloksia kokokudosnäytteissä, mikä saattaa johtua formaliinin aiheuttamasta silloittamisesta. Lisäksi testattiin kahta eri puskuria antigeenin paljastuksen optimointiin. Puskureissa näkyi suurempi eroavaisuus: PI15 näkyi paremmin pH 9-puskuria käytettäessä. Tulevaisuudessa myös transgeenisten hiirten sekä ihmisten eturauhaskudoksia tulisi testata PI15-värjäyksellä, jotta voitaisiin selvittää, onko PI15:llä roolia eturauhassyövän kehityksessä

Deformation equivariant cross-modality image synthesis with paired non-aligned training data

Funding Information: This work was supported by Research Council of Finland (Flagship programme: Finnish Center for Artificial Intelligence [grant 345552 ] and grants 315896 , 335976 , 336033 , 341967 , 352986 , 358246 ), ERA PerMed ABCAP (Research Council of Finland grants 334774 , 334782 ), EU (H2020 grant 101016775 and NextGenerationEU). We also acknowledge the computational resources provided by the Aalto Science-IT Project. | openaire: EC/H2020/101016775/EU//INTERVENECross-modality image synthesis is an active research topic with multiple medical clinically relevant applications. Recently, methods allowing training with paired but misaligned data have started to emerge. However, no robust and well-performing methods applicable to a wide range of real world data sets exist. In this work, we propose a generic solution to the problem of cross-modality image synthesis with paired but non-aligned data by introducing new deformation equivariance encouraging loss functions. The method consists of joint training of an image synthesis network together with separate registration networks and allows adversarial training conditioned on the input even with misaligned data. The work lowers the bar for new clinical applications by allowing effortless training of cross-modality image synthesis networks for more difficult data sets.Peer reviewe