bioRxiv Science⌕ Search

Biology subjects

Arslan, S.

Publications and source records attributed to Arslan, S..

8 recordsLinked to original sources

Direct In-Sample Sequencing of the 3' Transcriptome Expands the Capabilities of Optical Pooled Screens

We present a platform that directly sequences single guide RNAs and endogenous 3'UTRs in fixed cells while simultaneously measuring protein abundance and cellular morphology. We demonstrate platform capability by performing optical pooled screening of CRISPR-perturbed lung cancer cells. This approach unites direct in-sample RNA sequencing with complementary phenotypic readouts, enabling comprehensive, scalable, and functional genomics analyses within a single experiment.

genomics↗

Unraveling Tyrosine-Kinase Inhibitor Resistance in NSCLC Cells via Same-cell Measurement of RNA, Protein, and Morphological Responses

Non-small cell lung cancer (NSCLC) frequently develops resistance to tyrosine kinase inhibitors (TKIs), limiting the long-term success of targeted therapies. A deeper understanding of resistance mechanisms at the molecular and cellular levels may enable the development of more effective treatment strategies. Here, we applied the Teton detection assay on the AVITI24 platform to measure concurrently RNA, protein, and cellular morphology in NSCLC cell lines treated with the TKIs gefitinib and osimertinib. This single-cell, multiomic analysis revealed distinct expression and morphological profiles between drug-sensitive and resistant cells, including differences in MAPK-related pathway activity. Stratifying responses at the single-cell level uncovered subtle responses not detectable in bulk measurements. We identified CDK4/6 activity as a route of cell survival under TKI treatment and demonstrated that co-treatment with the CDK4/6 inhibitor palbociclib enhanced TKI efficacy. The ability to measure multiomics and cellular morphology in the same cells opens new avenues for future studies aimed at improving personalized treatment strategies in NSCLC and overcoming the obstacles posed by drug resistance.

genomics↗

Normal breast tissue classifiers assess large-scale tissue compartments with high accuracy

Cancer research emphasises early detection, yet quantitative methods for normal tissue analysis remain limited. Digitised haematoxylin and eosin (H&E)-stained slides enable computational histopathology, but artificial intelligence (AI)-based analysis of normal breast tissue (NBT) in whole slide images (WSIs) remains scarce. We curated 70 WSIs of NBTs from multiple sources and cohorts with pathologist-guided manual annotations of epithelium, stroma, and adipocytes (https://github.com/cancerbioinformatics/OASIS). We developed robust convolutional neural network (CNN)-based, patch-level classification models, named NBT-Classifiers, to tessellate and classify NBTs at different scales. Across three external cohorts, NBT-Classifiers trained on 128{square}x{square}128{square}{micro}m and 256{square}x{square}256{square}{micro}m patches achieved AUCs of 0.98-1.00. The model learned independent normal features different from those of precancerous and cancerous epithelium, which were further visualised using two explainable AI techniques. When integrated into an end-to-end preprocessing pipeline, NBT-Classifiers facilitate efficient downstream analysis within peri-lobular regions. NBT-Classifiers provide robust compartment-specific analytical tools and enhance our understanding of NBT appearances, which serve as valuable reference points for identifying premalignant changes and guiding early breast cancer prevention strategies.

pathology↗

Sequencing by avidity enables high accuracy with low reagent consumption

We present avidity sequencing - a novel sequencing chemistry that separately optimizes the process of stepping along a DNA template and the process of identifying each nucleotide within the template. Nucleotide identification uses multivalent nucleotide ligands on dye-labeled cores to form polymerase-polymer nucleotide complexes bound to clonal copies of DNA targets. These polymer-nucleotide substrates, termed avidites, decrease the required concentration of reporting nucleotides from micromolar to nanomolar, and yield negligible dissociation rates. We demonstrate the use of avidites as a key component of a sequencing technology that surpasses Q40 accuracy and enables a diversity of applications that include single cell RNA-seq and whole human genome sequencing. We also show the advantages of this technology in sequencing through long homopolymers.

genomics↗

Current Situation of Breeding Bird Species in the Gediz Delta, Turkey

Gediz Delta, one of the most important wetland ecosystems in the Mediterranean Basin, also is one of the most important areas for birds in Turkey. Breeding bird atlas studies were carried out in 2002, and 2006 and extensive breeding species were revealed. In 2021, we conducted an atlas of breeding birds using the same method as previous studies and observed the change over the last 15 years. Bird species seen and heard between May 3 and June 15 in 294 1x1 square kilometres were recorded and classified with internationally accepted breeding codes. Breeding codes were given to 113 species belonging to 48 families throughout the study. Of these, 32 species are classified as possible breeding, 34 species as probable breeding and 47 species as confirmed breeding. Threat factors of species and habitats are grouped under five main topics. According to previous studies, we observed that the number of breeding bird species increased. It is thought that the number of breeding bird species will increase with the elimination of threats and carrying out the necessary restoration works.

zoology↗

Large-scale systematic feasibility study on the pan-cancer predictability of multi-omic biomarkers from whole slide images with deep learning

We assessed the pan-cancer predictability of multi-omic biomarkers from haematoxylin and eosin (H&E)-stained whole slide images (WSI) using deep learning (DL) throughout a systematic study. A total of 13,443 DL models predicting 4,481 multi-omic biomarkers across 32 cancer types were trained and validated. The investigated biomarkers included a broad range of genetic, transcriptomic, proteomic, and metabolic alterations, as well as established markers relevant for prognosis, molecular subtypes and clinical outcomes. Overall, we found that DL can predict multi-omic biomarkers directly from routine histology images across solid cancer types, with 50% of the models performing at an area under the curve (AUC) of more than 0.633 (with 25% of the models having an AUC larger than 0.711). A wide range of biomarkers were detectable from routine histology images across all investigated cancer types, with a mean AUC of at least 0.62 in almost all malignancies. Strikingly, we observed that biomarker predictability was mostly consistent and not dependent on sample size and class ratio, suggesting a degree of true predictability inherent in histomorphology. Together, the results of our study show the potential of DL to predict a multitude of biomarkers across the omics spectrum using only routine slides. This paves the way for accelerating diagnosis and developing more precise treatments for cancer patients.

cancer biology↗

Evaluation of a predictive method for the H&E-based molecular profiling of breast cancer with deep learning

We present a public validation of PANProfiler (ER, PR, HER2), an in-vitro medical device (IVD) that predicts the qualitative status of estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) by analysing the hematoxylin and eosin (H&E)-stained tissue scan. In public validation on 648 (ER), 648 (PR) and 560 (HER2) unseen cases with known biomarker status, the device achieves an accuracy of 87% (ER), 83% (PR) and 87% (HER2). The validation offers early evidence of the ability to predict clinically relevant breast biomarkers from an H&E slide in a relevant clinical setting.

cancer biology↗

Major threats to a migratory raptor vary geographically along the eastern Mediterranean flyway

Millions of large soaring birds migrate from the Palaearctic to Africa every year, and follow distinct flyways around the Mediterranean Sea. While there is conservation concern for many long-distance migratory bird populations, the magnitude and geographic range of threats affecting birds along flyways are poorly known, which complicates efficient mitigation. We used an endangered soaring migrant, the Egyptian Vulture Neophron percnopterus, as an example species to assess important threats in 13 countries along the eastern Mediterranean flyway. We tracked 71 birds using satellite telemetry to quantify mortalities, surveyed 4198 km of powerlines to detect dead birds, conducted 910 interviews to quantify the prevalence of poison use, and assessed the magnitude of direct persecution by surveying markets and hunters. We lost 44 birds (50% in Europe and the Mediterranean Sea, 16% in the Middle East, and 34% in Africa), and mortality causes varied geographically. Inadvertent poisoning resulting from rural stakeholders targeting predators occurred along most of the flyway. On the breeding grounds in eastern Europe, poisoning and collision and electrocution continue to be major threats. Electrocution on small and poorly designed electricity pylons was most severe in Turkey, Ethiopia and Saudi Arabia, while direct persecution to meet market demands for belief-based use of vulture products appears to be the largest threat in Nigeria and Niger. Illegal direct persecution for leisure is a major threat in the Middle East and Egypt. Although our work cannot quantitatively estimate which of the identified threats has the greatest demographic impact on Egyptian Vultures, none of threats are species-specific and are therefore relevant for many other migratory birds. Our assessment highlights the key threats per country that range states need to address to meet their obligations under the Convention of Migratory Species to protect migratory birds.

zoology↗