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 provide a comparison of the performance of current WSI registration methods and guide researchers in selecting and developing methods.

Modeling type 1 diabetes progression using machine learning and single-cell transcriptomic measurements in human islets.

Type 1 diabetes (T1D) is a chronic condition in which beta cells are destroyed by immune cells. Despite progress in immunotherapies that could delay T1D onset, early detection of autoimmunity remains challenging. Here, we evaluate the utility of machine learning for early prediction of T1D using single-cell analysis of islets. Using gradient-boosting algorithms, we model changes in gene expression of single cells from pancreatic tissues in T1D and non-diabetic organ donors. We assess if mathematical modeling could predict the likelihood of T1D development in non-diabetic autoantibody-positive donors. While most autoantibody-positive donors are predicted to be non-diabetic, select donors with unique gene signatures are classified as T1D. Our strategy also reveals a shared gene signature in distinct T1D-associated models across cell types, suggesting a common effect of the disease on transcriptional outputs of these cells. Our study establishes a precedent for using machine learning in early detection of T1D.

Disease pathology signatures in a mouse model of Mucopolysaccharidosis type IIIB.

Mucopolysaccharidosis type IIIB (MPS IIIB) is a rare and devastating childhood-onset lysosomal storage disease caused by complete loss of function of the lysosomal hydrolase α-N-acetylglucosaminidase. The lack of functional enzyme in MPS IIIB patients leads to the progressive accumulation of heparan sulfate throughout the body and triggers a cascade of neuroinflammatory and other biochemical processes ultimately resulting in severe mental impairment and early death in adolescence or young adulthood. The low prevalence and severity of the disease has necessitated the use of animal models to improve our knowledge of the pathophysiology and for the development of therapeutic treatments. In this study, we took a systematic approach to characterizing a classical mouse model of MPS IIIB. Using a series of histological, biochemical, proteomic and behavioral assays, we tested MPS IIIB mice at two stages: during the pre-symptomatic and early symptomatic phases of disease development, in order to validate previously described phenotypes, explore new mechanisms of disease pathology and uncover biomarkers for MPS IIIB. Along with previous findings, this study helps provide a deeper understanding of the pathology landscape of this rare disease with high unmet medical need and serves as an important resource to the scientific community.

Pirh2-dependent DNA damage in neurons induced by the G-quadruplex ligand pyridostatin.

Noncanonical base pairing between four guanines (G) within single-stranded G-rich sequences leads to formation of а G-quartet. Self-stacking of G-quartets results in a columnar four-stranded DNA structure known as the G-quadruplex (G4 or G4-DNA). In cancer cells, G4-DNA regulates multiple DNA-dependent processes, including transcription, replication, and telomere function. How G4s function in neurons is poorly understood. Here, we performed a genome-wide gene expression analysis (RNA-Seq) to identify genes modulated by a G4-DNA ligand, pyridostatin (PDS), in primary cultured neurons. PDS promotes stabilization of G4 structures, thus allowing us to define genes directly or indirectly responsive to G4 regulation. We found that 901 genes were differentially expressed in neurons treated with PDS out of a total of 18,745 genes with measured expression. Of these, 505 genes were downregulated and 396 genes were upregulated and included gene networks regulating p53 signaling, the immune response, learning and memory, and cellular senescence. Within the p53 network, the E3 ubiquitin ligase Pirh2 (Rchy1), a modulator of DNA damage responses, was upregulated by PDS. Ectopically overexpressing Pirh2 promoted the formation of DNA double-strand breaks, suggesting a new DNA damage mechanism in neurons that is regulated by G4 stabilization. Pirh2 downregulated DDX21, an RNA helicase that unfolds G4-RNA and R-loops. Finally, we demonstrated that Pirh2 increased G4-DNA levels in the neuronal nucleolus. Our data reveal the genes that are responsive to PDS treatment and suggest similar transcriptional regulation by endogenous G4-DNA ligands. They also connect G4-dependent regulation of transcription and DNA damage mechanisms in neuronal cells.

Intrinsically disordered domain of transcription factor TCF-1 is required for T cell developmental fidelity.

In development, pioneer transcription factors access silent chromatin to reveal lineage-specific gene programs. The structured DNA-binding domains of pioneer factors have been well characterized, but whether and how intrinsically disordered regions affect chromatin and control cell fate is unclear. Here, we report that deletion of an intrinsically disordered region of the pioneer factor TCF-1 (termed L1) leads to an early developmental block in T cells. The few T cells that develop from progenitors expressing TCF-1 lacking L1 exhibit lineage infidelity distinct from the lineage diversion of TCF-1-deficient cells. Mechanistically, L1 is required for activation of T cell genes and repression of GATA2-driven genes, normally reserved to the mast cell and dendritic cell lineages. Underlying this lineage diversion, L1 mediates binding of TCF-1 to its earliest target genes, which are subject to repression as T cells develop. These data suggest that the intrinsically disordered N terminus of TCF-1 maintains T cell lineage fidelity.

scViewer: An Interactive Single-Cell Gene Expression Visualization Tool.

Single-cell RNA sequencing (scRNA-seq) is an attractive technology for researchers to gain valuable insights into the cellular processes and cell type diversity present in all tissues. The data generated by the scRNA-seq experiment are high-dimensional and complex in nature. Several tools are now available to analyze the raw scRNA-seq data from public databases; however, simple and easy-to-explore single-cell gene expression visualization tools focusing on differential expression and co-expression are lacking. Here, we present scViewer, an interactive graphical user interface (GUI) R/Shiny application designed to facilitate the visualization of scRNA-seq gene expression data. With the processed Seurat RDS object as input, scViewer utilizes several statistical approaches to provide detailed information on the loaded scRNA-seq experiment and generates publication-ready plots. The major functionalities of scViewer include exploring cell-type-specific gene expression, co-expression analysis of two genes, and differential expression analysis with different biological conditions considering both cell-level and subject-level variations using negative binomial mixed modeling. We utilized a publicly available dataset (brain cells from a study of Alzheimer's disease to demonstrate the utility of our tool. scViewer can be downloaded from GitHub as a Shiny app with local installation. Overall, scViewer is a user-friendly application that will allow researchers to visualize and interpret the scRNA-seq data efficiently for multi-condition comparison by performing gene-level differential expression and co-expression analysis on the fly. Considering the functionalities of this Shiny app, scViewer can be a great resource for collaboration between bioinformaticians and wet lab scientists for faster data visualizations.

Efficient quality control of whole slide pathology images with human-in-the-loop training.

Histopathology whole slide images (WSIs) are being widely used to develop deep learning-based diagnostic solutions, especially for precision oncology. Most of these diagnostic softwares are vulnerable to biases and impurities in the training and test data which can lead to inaccurate diagnoses. For instance, WSIs contain multiple types of tissue regions, at least some of which might not be relevant to the diagnosis. We introduce HistoROI, a robust yet lightweight deep learning-based classifier to segregate WSI into 6 broad tissue regions-epithelium, stroma, lymphocytes, adipose, artifacts, and miscellaneous. HistoROI is trained using a novel human in-the-loop and active learning paradigm that ensures variations in training data for labeling efficient generalization. HistoROI consistently performs well across multiple organs, despite being trained on only a single dataset, demonstrating strong generalization. Further, we have examined the utility of HistoROI in improving the performance of downstream deep learning-based tasks using the CAMELYON breast cancer lymph node and TCGA lung cancer datasets. For the former dataset, the area under the receiver operating characteristic curve (AUC) for metastasis versus normal tissue of a neural network trained using weakly supervised learning increased from 0.88 to 0.92 by filtering the data using HistoROI. Similarly, the AUC increased from 0.88 to 0.93 for the classification between adenocarcinoma and squamous cell carcinoma on the lung cancer dataset. We also found that the performance of the HistoROI improves upon HistoQC for artifact detection on a test dataset of 93 annotated WSIs. The limitations of the proposed model are analyzed, and potential extensions are also discussed.

The Tardigrade damage suppressor protein Dsup promotes DNA damage in neurons.

Tardigrades are microscopic invertebrates, which are capable of withstanding extreme environmental conditions, including high levels of radiation. A Tardigrade protein, Dsup (Damage Suppressor), protects the Tardigrade's DNA during harsh environmental stress and X-rays. When expressed in cancer cells, Dsup protects DNA from single- and double-strand breaks (DSBs) induced by radiation, increases survival of irradiated cells, and protects DNA from reactive oxygen species. These unusual properties of Dsup suggested that understanding how the protein functions may help in the design of small molecules that could protect humans during radiotherapy or space travel. Here, we investigated if Dsup is protective in cortical neurons cultured from rat embryos. We discovered that, in cortical neurons, the codon-optimized Dsup localizes to the nucleus and, surprisingly, promotes neurotoxicity, leading to neurodegeneration. Unexpectedly, we found that Dsup expression results in the formation of DNA DSBs in cultured neurons. With electron microscopy, we discovered that Dsup promotes chromatin condensation. Unlike Dsup's protective properties in cancerous cells, in neurons, Dsup promotes neurotoxicity, induces DNA damage, and rearranges chromatin. Neurons are sensitive to Dsup, and Dsup is a doubtful surrogate for DNA protection in neuronal cells.

Alternative splicing of the SUMO1/2/3 transcripts affects cellular SUMOylation and produces functionally distinct SUMO protein isoforms.

Substantial increases in the conjugation of the main human SUMO paralogs, SUMO1, SUMO2, and SUMO3, are observed upon exposure to different cellular stressors, and such increases are considered important to facilitate cell survival to stress. Despite their critical cellular role, little is known about how the levels of the SUMO modifiers are regulated in the cell, particularly as it relates to the changes observed upon stress. Here we characterize the contribution of alternative splicing towards regulating the expression of the main human SUMO paralogs under normalcy and three different stress conditions, heat-shock, cold-shock, and Influenza A Virus infection. Our data reveal that the normally spliced transcript variants are the predominant mature mRNAs produced from the SUMO genes and that the transcript coding for SUMO2 is by far the most abundant of all. We also provide evidence that alternatively spliced transcripts coding for protein isoforms of the prototypical SUMO proteins, which we refer to as the SUMO alphas, are also produced, and that their abundance and nuclear export are affected by stress in a stress- and cell-specific manner. Additionally, we provide evidence that the SUMO alphas are actively synthesized in the cell as their coding mRNAs are found associated with translating ribosomes. Finally, we provide evidence that the SUMO alphas are functionally different from their prototypical counterparts, with SUMO1α and SUMO2α being non-conjugatable to protein targets, SUMO3α being conjugatable but targeting a seemingly different subset of protein from those targeted by SUMO3, and all three SUMO alphas displaying different cellular distributions from those of the prototypical SUMOs. Thus, alternative splicing appears to be an important contributor to the regulation of the expression of the SUMO proteins and the cellular functions of the SUMOylation system.

A simple drying solution that minimizes cracking during air-drying of polyacrylamide gels.

Sodium dodecyl sulfate-polyacrylamide gel electrophoresis is a routine technique used in biochemistry. Air-drying is an economical method of gel preservation that does not require expensive equipment. Our laboratory uses drying frames from RPI, which recommends a drying solution of 20% ethanol and 10% glycerol. The solution performs well for gels up to 10% acrylamide and 0.75 mm thickness; however, crack formation may occur if nicks or bubbles are present. The literature shows various drying methods and combinations of alcohol (30-100%) and glycerol (5-35%), but still reports cracking problems. Tests were conducted to independently evaluate the effects of ethanol and glycerol concentration on gel cracking. Here we introduce a simple solution that does not require glycerol or modified frames to generate preserved, crack-free sodium dodecyl sulfate-polyacrylamide gel electrophoresis gels.

Computational workflow and interactive analysis of single-cell expression profiling of islets generated by the Human Pancreas Analysis Program.

Type 1 and Type 2 diabetes are distinct genetic diseases of the pancreas which are defined by the abnormal level of blood glucose. Understanding the initial molecular perturbations that occur during the pathogenesis of diabetes is of critical importance in understanding these disorders. The inability to biopsy the human pancreas of living donors hampers insights into early detection, as the majority of diabetes studies have been performed on peripheral leukocytes from the blood, which is not the site of pathogenesis. Therefore, efforts have been made by various teams including the Human Pancreas Analysis Program (HPAP) to collect pancreatic tissues from deceased organ donors with different clinical phenotypes. HPAP is designed to define the molecular pathogenesis of islet dysfunction by generating detailed datasets of functional, cellular, and molecular information in pancreatic tissues of clinically well-defined organ donors with Type 1 and Type 2 diabetes. Moreover, data generated by HPAP continously become available through a centralized database, PANC-DB, thus enabling the diabetes research community to access these multi-dimensional data prepublication. Here, we present the computational workflow for single-cell RNA-seq data analysis of 258,379 high-quality cells from the pancreatic islets of 67 human donors generated by HPAP, the largest existing scRNA-seq dataset of human pancreatic tissues. We report various computational steps including preprocessing, doublet removal, clustering and cell type annotation across single-cell RNA-seq data from islets of four distintct classes of organ donors, i.e. non-diabetic control, autoantibody positive but normoglycemic, Type 1 diabetic, and Type 2 diabetic individuals. Moreover, we present an interactive tool, called CellxGene developed by the Chan Zuckerberg initiative, to navigate these high-dimensional datasets. Our data and interactive tools provide a reliable reference for singlecell pancreatic islet biology studies, especially diabetes-related conditions.

Preparation, Characterization, and Evaluation of Emission and Performance Characteristics of Thumba Methyl Ester (Biodiesel).

Thumba oil with a higher triglyceride content can be a promising feed for synthesizing a fatty acid alkyl ester as an alternative to pure diesel. The current study investigates the emission and performance characteristics of thumba methyl ester (TME) in compression ignition (CI) engines corresponding to variable loads and compression ratios (CRs), respectively. TME was prepared at an optimized pressure of 5 bar by hydrodynamic cavitation. The properties of TME-diesel blends with varied volume percentages of biodiesel, such as 5, 10, 15, 20, and 25, denoted B5, B10, B15, B20, and B25, respectively, were compared to pure TME (100% biodiesel) and pure diesel (100%). The B20 biodiesel blend has been observed as the optimal one based on the lower emission composition and higher brake thermal efficiency. For B20 fuel, injection at 23° before the top dead center (TDC) and a CR of 18 resulted in the lowest brake specific fuel consumption of 0.32 kg/kW h and a maximum brake thermal efficiency of 36.5%. Using titanium dioxide nanoparticles in the pre-stage of TME manufacturing has ultimately reduced the nitrogen oxide, hydrocarbon, and carbon monoxide emissions. At a CR of 18 and advanced injection 23° before TDC for a CI engine, TME derived from thumba oil has the potential to be a viable diesel substitute.

Author's Reply to "MoNuSAC2020: A Multi-Organ Nuclei Segmentation and Classification Challenge".

We had released MoNuSAC2020 as one of the largest publicly available, manually annotated, curated, multi-class, and multi-instance medical image segmentation datasets. Based on this dataset, we had organized a challenge at the International Symposium on Biomedical Imaging (ISBI) 2020. Along with the challenge participants, we had published an article summarizing the results and findings of the challenge (Verma et al., 2021). Foucart et al. (2022) in their "Analysis of the MoNuSAC 2020 challenge evaluation and results: metric implementation errors" have pointed ways in which the computation of the segmentation performance metric for the challenge can be corrected or improved. After a careful examination of their analysis, we have found a small bug in our code and an erroneous column-header swap in one of our result tables. Here, we present our response to their analysis, and issue an errata. After fixing the bug the challenge rankings remain largely unaffected. On the other hand, two of Foucart et al.'s other suggestions are good for future consideration, but it is not clear that those should be immediately implemented. We thank Foucart et al. for their detailed analysis to help us fix the two errors.

Single-cell multi-omics analysis of human pancreatic islets reveals novel cellular states in type 1 diabetes.

Type 1 diabetes (T1D) is an autoimmune disease in which immune cells destroy insulin-producing beta cells. The aetiology of this complex disease is dependent on the interplay of multiple heterogeneous cell types in the pancreatic environment. Here, we provide a single-cell atlas of pancreatic islets of 24 T1D, autoantibody-positive and nondiabetic organ donors across multiple quantitative modalities including ~80,000 cells using single-cell transcriptomics, ~7,000,000 cells using cytometry by time of flight and ~1,000,000 cells using in situ imaging mass cytometry. We develop an advanced integrative analytical strategy to assess pancreatic islets and identify canonical cell types. We show that a subset of exocrine ductal cells acquires a signature of tolerogenic dendritic cells in an apparent attempt at immune suppression in T1D donors. Our multimodal analyses delineate cell types and processes that may contribute to T1D immunopathogenesis and provide an integrative procedure for exploration and discovery of human pancreatic function.

MoNuSAC2020: A Multi-Organ Nuclei Segmentation and Classification Challenge.

Detecting various types of cells in and around the tumor matrix holds a special significance in characterizing the tumor micro-environment for cancer prognostication and research. Automating the tasks of detecting, segmenting, and classifying nuclei can free up the pathologists' time for higher value tasks and reduce errors due to fatigue and subjectivity. To encourage the computer vision research community to develop and test algorithms for these tasks, we prepared a large and diverse dataset of nucleus boundary annotations and class labels. The dataset has over 46,000 nuclei from 37 hospitals, 71 patients, four organs, and four nucleus types. We also organized a challenge around this dataset as a satellite event at the International Symposium on Biomedical Imaging (ISBI) in April 2020. The challenge saw a wide participation from across the world, and the top methods were able to match inter-human concordance for the challenge metric. In this paper, we summarize the dataset and the key findings of the challenge, including the commonalities and differences between the methods developed by various participants. We have released the MoNuSAC2020 dataset to the public.

Role of Stress-Survival Pathways and Transcriptomic Alterations in Progression of Colorectal Cancer: A Health Disparities Perspective.

Every year, more than a million individuals are diagnosed with colorectal cancer (CRC) across the world. Certain lifestyle and genetic factors are known to drive the high incidence and mortality rates in some groups of individuals. The presence of enormous amounts of reactive oxygen species is implicated for the on-set and carcinogenesis, and oxidant scavengers are thought to be important in CRC therapy. In this review, we focus on the ethnicity-based CRC disparities in the U.S., the negative effects of oxidative stress and apoptosis, and gene regulation in CRC carcinogenesis. We also highlight the use of antioxidants for CRC treatment, along with screening for certain regulatory genetic elements and oxidative stress indicators as potential biomarkers to determine the CRC risk and progression.

Identification of Hub Genes in Different Stages of Colorectal Cancer through an Integrated Bioinformatics Approach.

Colorectal cancer (CRC) is the third most common cancer that contributes to cancer-related morbidity. However, the differential expression of genes in different phases of CRC is largely unknown. Moreover, very little is known about the role of stress-survival pathways in CRC. We sought to discover the hub genes and identify their roles in several key pathways, including oxidative stress and apoptosis in the different stages of CRC. To identify the hub genes that may be involved in the different stages of CRC, gene expression datasets were obtained from the gene expression omnibus (GEO) database. The differentially expressed genes (DEGs) common among the different datasets for each group were obtained using the robust rank aggregation method. Then, gene enrichment analysis was carried out with Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases. Finally, the protein-protein interaction networks were constructed using the Cytoscape software. We identified 40 hub genes and performed enrichment analysis for each group. We also used the Oncomine database to identify the DEGs related to stress-survival and apoptosis pathways involved in different stages of CRC. In conclusion, the hub genes were found to be enriched in several key pathways, including the cell cycle and p53 signaling pathway. Some of the hub genes were also reported in the stress-survival and apoptosis pathways. The hub DEGs revealed from our study may be used as biomarkers and may explain CRC development and progression mechanisms.

A cyclical deep learning based framework for simultaneous inverse and forward design of nanophotonic metasurfaces.

The conventional approach to nanophotonic metasurface design and optimization for a targeted electromagnetic response involves exploring large geometry and material spaces. This is a highly iterative process based on trial and error, which is computationally costly and time consuming. Moreover, the non-uniqueness of structural designs and high non-linearity between electromagnetic response and design makes this problem challenging. To model this unintuitive relationship between electromagnetic response and metasurface structural design as a probability distribution in the design space, we introduce a framework for inverse design of nanophotonic metasurfaces based on cyclical deep learning (DL). The proposed framework performs inverse design and optimization mechanism for the generation of meta-atoms and meta-molecules as metasurface units based on DL models and genetic algorithm. The framework includes consecutive DL models that emulate both numerical electromagnetic simulation and iterative processes of optimization, and generate optimized structural designs while simultaneously performing forward and inverse design tasks. A selection and evaluation of generated structural designs is performed by the genetic algorithm to construct a desired optical response and design space that mimics real world responses. Importantly, our cyclical generation framework also explores the space of new metasurface topologies. As an example application of the utility of our proposed architecture, we demonstrate the inverse design of gap-plasmon based half-wave plate metasurface for user-defined optical response. Our proposed technique can be easily generalized for designing nanophtonic metasurfaces for a wide range of targeted optical response.

A network-based zoning for parallel whole-cell simulation.

Motivation: In Computational Cell Biology, whole-cell modeling and simulation is an absolute requirement to analyze and explore the cell of an organism. Despite few individual efforts on modeling, the prime obstacle hindering its development and progress is its compute-intensive nature. Towards this end, little knowledge is available on how to reduce the enormous computational overhead and which computational systems will be of use. Results: In this article, we present a network-based zoning approach that could potentially be utilized in the parallelization of whole-cell simulations. Firstly, we construct the protein-protein interaction graph of the whole-cell of an organism using experimental data from various sources. Based on protein interaction information, we predict protein locality and allocate confidence score to the interactions accordingly. We then identify the modules of strictly localized interacting proteins by performing interaction graph clustering based on the confidence score of the interactions. By applying this method to Escherichia coli K12, we identified 188 spatially localized clusters. After a thorough Gene Ontology-based analysis, we proved that the clusters are also in functional proximity. We then conducted Principal Coordinates Analysis to predict the spatial distribution of the clusters in the simulation space. Our automated computational techniques can partition the entire simulation space (cell) into simulation sub-cells. Each of these sub-cells can be simulated on separate computing units of the High-Performance Computing (HPC) systems. We benchmarked our method using proteins. However, our method can be extended easily to add other cellular components like DNA, RNA and metabolites. Availability and implementation: . Supplementary information: Supplementary data are available at Bioinformatics online.