In numerous image-based studies, such as cell studies, a plethora of information is embedded in cell objects, which must first be accurately extracted before it can be utilized for further downstream analysis and linked to biological processes explored in the original experiment. One such technique for this purpose is instance segmentation. In our study, which cell death and associated pathways and regulatory networks are investigated through a genome-wide microscopic screen, the dataset is extensive and challenging to manually curate or annotate. Therefore, we opted to train distinct U-shape nuclei segmentation models using only a small but meticulously annotated subset of the high-content screen data to ensure high accuracy where the superior U-Net model achieved an F1-score of 89%. Subsequently, we have deployed the best U-Net model to segment whole objects in the nuclei channel of the original genome-wide dataset, comprising nearly 5 million image frames across 213 plates. The extracted features from the nuclei channel served as biological metrics to explore cell death. The results identified several well-known genes involved in established cell death pathways, such as apoptosis. Additionally, other genes were discovered that merit further investigation for their effects on altering the visual features of cell nuclei.

Cell death plays a pivotal role in many physiological processes, such as cell homeostasis, embryonic development, immune defence and in the pathophysiology of numerous diseases, such as cancer, infections and degenerative diseases. However, the lack of a comprehensive and up-to-date resource on cell death regulators poses a significant challenge to researchers in the field. Existing databases are often limited in scope, differ in content and are updated irregularly. This deficiency impedes progress in understanding the intricate molecular mechanisms governing cell death and hampers the development of targeted therapies. To address this, we have performed a census of the existing cell death databases as well as the cell death-associated entries in the UniProt and Gene Ontology databases. To ensure high quality, we have focused on manually curated entries rather than those created from automatic prediction tools. The results have been consolidated into a joint database of the known cell death regulators, including both proteins and non-coding RNAs. The Cell Death Census 2024 results and the associated python code for database parsing, cleaning and merging is publicly available at . ### Competing Interest Statement The authors have declared no competing interest.

Many forms of bioimage analysis involve the detection of objects and their outlines. In the context of microscopy-based high-throughput drug and genomic screening and even in smaller scale microscopy experiments, the objects that most often need to be detected are cells. In order to develop and benchmark algorithms and neural networks that can perform this task, high-quality datasets with annotated cell outlines are needed.We have created a dataset, named Aitslab_bioimaging2, consisting of 60 fluorescence microscopy images with EGFP-Galectin-3 labelled cells and their hand-labelled outlines. Images were acquired on a Thermo Fischer CX7 high-content imaging system at 20x magnification created as part of an RNA interference screen with a modified U2OS osteosarcoma cell line. Outlines were labelled by three annotators, who had high inter-annotator agreement between them and with a biomedical expert, who labelled some of the objects for comparison and reviewed a subset of the labels, making minor corrections as needed.The dataset comprises over 2200 annotated cell objects in total, making it sufficient in size to train high-performing neural networks for instance or semantic segmentation. Labels can also easily be converted to boxes for object detection tasks. The dataset is already pre-divided into training, development, and test sets. Matching nuclear staining and outlines are available for part of the dataset from a previous publication (dataset Aitslab_bioimaging1) [1].

Acute respiratory distress syndrome (ARDS) is a life-threatening condition with mortality rates between 30-50%. Although in vitro models replicate some aspects of ARDS, small and large animal models remain the primary research tools due to the multifactorial nature of the disease. When using these animal models, histology serves as the gold standard method to confirm lung injury and exclude other diagnoses as high-resolution chest images are often not feasible. Semi-quantitative scoring performed by independent observers is the most common form of histologic analysis in pre-clinical animal models of ARDS. Despite progress in standardizing analysis procedures, objectively comparing histological injuries remains challenging, even for highly-trained pathologists. Standardized scoring simplifies the task and allows better comparisons between research groups and across different injury models, but it is time-consuming, and interobserver variability remains a significant concern. Convolutional neural networks (CNNs), which have emerged as a key tool in image analysis, could automate this process, potentially enabling faster and more reproducible analysis. Here we explored the reproducibility of human standardized scoring for an animal model of ARDS and its suitability for training CNNs for automated scoring at the whole slide level. We found large variations between human scorers, even for pre-clinical experts and board-certified pathologies in evaluating ARDS animal models. We demonstrate that CNNs (VGG16, EfficientNetB4) are suitable for automated scoring and achieve up to 83% F1-score and 78% accuracy. Thus, CNNs for histopathological classification of acute lung injury could help reduce human variability and eliminate a time-consuming manual research task with acceptable performance.

Background Medical and life science research generates millions of publications, and it is a great challenge for researchers to utilize this information in full since its scale and complexity greatly surpasses human reading capabilities. Automated text mining can help extract and connect information spread across this large body of literature, but this technology is not easily accessible to life scientists. Methods and Results Here, we developed an easy-to-use end-to-end pipeline for deep learning- and dictionary-based named entity recognition (NER) of typical entities found in medical and life science research articles, including diseases, cells, chemicals, genes/proteins, species and others. The pipeline can access and process large medical research article collections (PubMed, CORD-19) or raw text and incorporates a series of deep learning models fine-tuned on the HUNER corpora collection. In addition, the pipeline can perform dictionary-based NER related to COVID-19 and other medical topics. Users can also load their own NER models and dictionaries to include additional entities. The output consists of publication-ready ranked lists and graphs of detected entities and files containing the annotated texts. In addition, we provide two accessory scripts which allow processing of files in PubTator format and rapid inspection of the results for specific entities of interest. As model use cases, the pipeline was deployed on two collections of autophagy-related abstracts from PubMed and on the CORD19 dataset, a collection of 764 398 research article abstracts related to COVID-19. Conclusions The NER pipeline we present is applicable in a variety of medical research settings and makes customizable text mining accessible to life scientists.

Electronic health records (EHRs) are a rich source of information for medical research and public health monitoring. Information systems based on EHR data could also assist in patient care and hospital management. However, much of the data in EHRs is in the form of unstructured text, which is difficult to process for analysis. Natural language processing (NLP), a form of artificial intelligence, has the potential to enable automatic extraction of information from EHRs and several NLP tools adapted to the style of clinical writing have been developed for English and other major languages. In contrast, the development of NLP tools for less widely spoken languages such as Swedish has lagged behind. A major bottleneck in the development of NLP tools is the restricted access to EHRs due to legitimate patient privacy concerns. To overcome this issue we have generated a citizen science platform for collecting artificial Swedish EHRs with the help of Swedish physicians and medical students. These artificial EHRs describe imagined but plausible emergency care patients in a style that closely resembles EHRs used in emergency departments in Sweden. In the pilot phase, we collected a first batch of 50 artificial EHRs, which has passed review by an experienced Swedish emergency care physician. We make this dataset publicly available as OpenChart-SE corpus (version 1) under an open-source license for the NLP research community. The project is now open for general participation and Swedish physicians and medical students are invited to submit EHRs on the project website (https://github.com/Aitslab/openchart-se), where additional batches of quality-controlled EHRs will be released periodically. Dataset content OpenChart-SE, version 1 corpus (txt files and and dataset.csv) The OpenChart-SE corpus, version 1, contains 50 artificial EHRs (note that the numbering starts with 5 as 1-4 were test cases that were not suitable for publication). The EHRs are available in two formats, structured as a .csv file and as separate textfiles for annotation. Note that flaws in the data were not cleaned up so that it simulates what could be encountered when working with data from different EHR systems. All charts have been checked for medical validity by a resident in Emergency Medicine at a Swedish hospital before publication. Codebook.xlsx The codebook contain information about each variable used. It is in XLSForm-format, which can be re-used in several different applications for data collection. suppl_data_1_openchart-se_form.pdf OpenChart-SE mock emergency care EHR form. suppl_data_3_openchart-se_dataexploration.ipynb This jupyter notebook contains the code and results from the analysis of the OpenChart-SE corpus. More details about the project and information on the upcoming preprint accompanying the dataset can be found on the project website (https://github.com/Aitslab/openchart-se).

Automated detection of cell nuclei in fluorescence microscopy images is a key task in bioimage analysis. It is essential for most types of microscopy-based high-throughput drug and genomic screening and is often required in smaller scale experiments as well. To develop and evaluate algorithms and neural networks that perform instance or semantic segmentation for detecting nuclei, high quality annotated data is essential. Here we present a benchmarking dataset of fluorescence microscopy images with Hoechst 33342-stained nuclei together with annotations of nuclei, nuclear fragments and micronuclei. Images were randomly selected from an RNA interference screen with a modified U2OS osteosarcoma cell line, acquired on a Thermo Fischer CX7 high-content imaging system at 20x magnification. Labelling was performed by a single annotator and reviewed by a biomedical expert. The dataset, called Aitslab-bioimaging1, contains 50 images showing over 2000 labelled nuclear objects in total, which is sufficiently large to train well-performing neural networks for instance or semantic segmentation. The dataset is split into training, development and test set for user convenience.

Here we present a toolbox for natural language processing tasks related to SARS-CoV-2. It comprises English dictionaries of synonyms for SARS-CoV-2 and COVID-19, a silver standard corpus generated with the dictionaries and a gold standard corpus of 10 Pubmed abstracts manually annotated for disease, virus, symptom and protein/gene terms. This toolbox is freely available on github (on this https URL) and can be used for text analytics in a variety of settings related to the COVID-19 crisis. It will be expanded and applied in NLP tasks over the next weeks and the community is invited to contribute.

Loss of lysosomal membrane integrity, often referred to as lysosomal membrane permeabilization (LMP), occurs in many instances of cell death either as an initiating or as an amplifying event. Currently, the best method for detecting LMP is the galectin puncta formation assay which can be used for a broad range of sample types, both fixed and live, is easy to perform, and highly sensitive. This method, which is similar to the widely used LC3 puncta formation assay for autophagy, is based on the translocation of galectins to damaged lysosomes resulting in a change from uniform to punctate staining pattern. Here, we provide protocols for the galectin puncta formation assay in fixed and live cells and for an alternative assay based on fluorescent dextran release from damaged lysosomes, which can be performed in parallel.

Literature, tools and other resources for microscopy-based high-content screening (HCS), also referred to as high-content analysis (HCA) or high-content imaging (HCI). Covered areas include microscopy theory, screening procedures, image analysis, phenotyp

The lysosomal cell death (LCD) pathway is a caspase 3-independent cell death pathway that has been suggested as a possible target for cancer therapy, making the development of sensitive and specific high-throughput (HT) assays to identify LCD inducers highly desirable. In this study, we report a two-step HT screening platform to reliably identify such molecules. First, using a robust HT primary screen based on propidium iodide uptake, we identified compounds that kill through nonapoptotic pathways. A phenotypic image-based assay using a galectin-3 (Gal-3) reporter was then used to further classify hits based on lysosomal permeabilization, a hallmark of LCD. The identification of permeabilized lysosomes in our image-based assay is not affected by changes in the lysosomal pH, thus resolving an important limitation in currently used methods. We have validated our platform in a screen by identifying 24 LCD inducers, some previously known to induce LCD. Although most LCD inducers were cationic amphiphilic drugs (CADs), we have also identified a non-CAD LCD inducer, which is of great interest in the field. Our data also gave new insights into the biology of LCD, suggesting that lysosomal accumulation and acid sphingomyelinase inhibition are not sufficient or necessary for the induction of LCD. Overall, our results demonstrate a robust HT platform to identify novel LCD inducers that will also be very useful for gaining deeper insights into the molecular mechanism of LCD induction.

Lysosomal membrane permeabilization (LMP) contributes to tissue involution, degenerative diseases, and cancer therapy. Its investigation has, however, been hindered by the lack of sensitive methods. Here, we characterize and validate the detection of galectin puncta at leaky lysosomes as a highly sensitive and easily manageable assay for LMP. LGALS1/galectin-1 and LGALS3/galectin-3 are best suited for this purpose due to their widespread expression, rapid translocation to leaky lysosomes and availability of high-affinity antibodies. Galectin staining marks individual leaky lysosomes early during lysosomal cell death and is useful when defining whether LMP is a primary or secondary cause of cell death. This sensitive method also reveals that cells can survive limited LMP and confirms a rapid formation of autophagic structures at the site of galectin puncta. Importantly, galectin staining detects individual leaky lysosomes also in paraffin-embedded tissues allowing us to demonstrate LMP in tumor xenografts in mice treated with cationic amphiphilic drugs and to identify a subpopulation of lysosomes that initiates LMP in involuting mouse mammary gland. The use of ectopic fluorescent galectins renders the galectin puncta assay suitable for automated screening and visualization of LMP in live cells and animals. Thus, the lysosomal galectin puncta assay opens up new possibilities to study LMP in cell death and its role in other cellular processes such as autophagy, senescence, aging, and inflammation.

Lysosomal cell death is triggered by lysosomal membrane permeabilization (LMP) and subsequent release of lysosomal hydrolases from the lysosomal lumen into the cytosol. Once released into the cytosol, the lysosomal cathepsin proteases act as executioner proteases for the subsequent cell death-either autonomously without caspase activation or in concert with the classical apoptotic machinery. Lysosomal cell death usually remains functional in apoptosis-resistant cancer cells and thus holds great potential as a therapeutic strategy for circumventing apoptosis deficiency in cancers. Notably, lysosomal cell death also plays an important role in normal physiology, e. g., during the regression of the mammary gland. Here we present four complementary methods for the quantification and visualization of LMP during the onset of death: (1) enzymatic activity measurements of released lysosomal hydrolases in the cytosol after digitonin extraction, (2) direct visualization of LMP by monitoring the release of fluorescent dextran from lysosomes into the cytosol, (3) immunocytochemistry to detect cathepsins released into the cytosol, and (4) detection of the translocation of galectins to damaged lysosomes. The methods presented here can ideally be combined as needed to provide solid evidence for LMP after a given cytotoxic stimuli.

HAMLET is the first member of a new family of tumoricidal protein–lipid complexes that kill cancer cells broadly, while sparing healthy, differentiated cells. Many and diverse tumor cell types are sensitive to the lethal effect, suggesting that HAMLET identifies and activates conserved death pathways in cancer cells. Here, we investigated the molecular basis for the difference in sensitivity between cancer cells and healthy cells. Using a combination of small-hairpin RNA (shRNA) inhibition, proteomic and metabolomic technology, we identified the c-Myc oncogene as one essential determinant of HAMLET sensitivity. Increased c-Myc expression levels promoted sensitivity to HAMLET and shRNA knockdown of c-Myc suppressed the lethal response, suggesting that oncogenic transformation with c-Myc creates a HAMLET-sensitive phenotype. Furthermore, HAMLET sensitivity was modified by the glycolytic state of tumor cells. Glucose deprivation sensitized tumor cells to HAMLET-induced cell death and in the shRNA screen, hexokinase 1 (HK1), 6-phosphofructo-2-kinase/fructose-2,6-biphosphatase 1 and hypoxia-inducible factor 1α modified HAMLET sensitivity. HK1 was shown to bind HAMLET in a protein array containing ∼8000 targets, and HK activity decreased within 15 min of HAMLET treatment, before morphological signs of tumor cell death. In parallel, HAMLET triggered rapid metabolic paralysis in carcinoma cells. Tumor cells were also shown to contain large amounts of oleic acid and its derivatives already after 15 min. The results identify HAMLET as a novel anti-cancer agent that kills tumor cells by exploiting unifying features of cancer cells such as oncogene addiction or the Warburg effect.

Summary Lysosomes serve as the cellular recycling centre and are filled with numerous hydrolases that can degrade most cellular macromolecules. Lysosomal membrane permeabilization and the consequent leakage of the lysosomal content into the cytosol leads to so-called “lysosomal cell death”. This form of cell death is mainly carried out by the lysosomal cathepsin proteases and can have necrotic, apoptotic or apoptosis-like features depending on the extent of the leakage and the cellular context. This article summarizes our current knowledge on lysosomal cell death with an emphasis on the upstream mechanisms that lead to lysosomal membrane permeabilization.

Microtubule-disturbing drugs inhibit lysosomal trafficking and induce lysosomal membrane permeabilization followed by cathepsin-dependent cell death. To identify specific trafficking-related proteins that control cell survival and lysosomal stability, we screened a molecular motor siRNA library in human MCF7 breast cancer cells. SiRNAs targeting four kinesins (KIF11/Eg5, KIF20A, KIF21A, KIF25), myosin 1G (MYO1G), myosin heavy chain 1 (MYH1) and tropomyosin 2 (TPM2) were identified as effective inducers of non-apoptotic cell death. The cell death induced by KIF11, KIF21A, KIF25, MYH1 or TPM2 siRNAs was preceded by lysosomal membrane permeabilization, and all identified siRNAs induced several changes in the endo-lysosomal compartment, i.e. increased lysosomal volume (KIF11, KIF20A, KIF25, MYO1G, MYH1), increased cysteine cathepsin activity (KIF20A, KIF25), altered lysosomal localization (KIF25, MYH1, TPM2), increased dextran accumulation (KIF20A), or reduced autophagic flux (MYO1G, MYH1). Importantly, all seven siRNAs also killed human cervix cancer (HeLa) and osteosarcoma (U-2-OS) cells and sensitized cancer cells to other lysosome-destabilizing treatments, i.e. photo-oxidation, siramesine, etoposide or cisplatin. Similarly to KIF11 siRNA, the KIF11 inhibitor monastrol induced lysosomal membrane permeabilization and sensitized several cancer cell lines to siramesine. While KIF11 inhibitors are under clinical development as mitotic blockers, our data reveal a new function for KIF11 in controlling lysosomal stability and introduce six other molecular motors as putative cancer drug targets.