Biology subjects

Sultan, S.

Publications and source records attributed to Sultan, S..

6 recordsLinked to original sources

bioRxiv2026

A human-in-the-loop approach for faster cell tracking

Traditional cell tracking methods focus on fully automated tracking. The quality of the generated tracks, however, is heavily influenced by factors such as cell speed and density. In complex scenarios where track correctness is essential, an efficient and reliable tool for manual track correction remains a key component of the cell tracking process. Most tracking tools that allow for such manual correction do so only as a post-processing step, using human feedback to correct one track error at a time -- without improving the quality of other tracks in the dataset. This highlights the need for a more efficient track correction method that iteratively integrates human feedback into the automated tracking process. In this paper we propose a human-in-the-loop approach to cell tracking and investigate the extent to which a tracking algorithm can learn from human input during the tracking process. We combine a graph representation of cell detections, Dijkstra's shortest path algorithm, and a neural network that predicts cell displacement to generate initial track suggestions. We introduce a tool that enables human feedback on individual tracks suggested by the algorithm; this feedback is then propagated to improve subsequent track generation. Compared to the traditional approach of fully automated tracking with correction as a post-processing step, our human-in-the-loop tracking framework reduces the number of corrections required. Finally, we show how this approach can be integrated with existing state-of-the-art tracking algorithms by using pre-generated tracks as a prior, improving the quality of tracks suggested to the human annotator.

bioinformatics↗

bioRxiv2024

Astrocyte-derived PEA116 increases adult hippocampal neurogenesis and confers stress resilience

In the dentate gyrus of the hippocampus, the neurogenic niche regulates several steps of adult neurogenesis, from the proliferation to the integration of newly formed neurons in the hippocampal network. However, the role of astrocytes in the regulation of adult neural stem cell (aNSC) proliferation is still little described. Here, we found that blocking vesicular release from astrocytes decreased cell proliferation in the dentate gyrus, resulting in impaired adult neurogenesis. Inversely, astrocyte-conditioned medium increased cell proliferation in a vesicular release-dependent manner. We identified PEA116 as a peptide released by astrocytes, that is derived from the c-terminal portion of the PEA15 protein and increased cell proliferation. PEA116 increased ERK2 phosphorylation, decreased the expression of genes involved in aNSC quiescence, resulting in aNSC quiescence exit. The ensuing increase in hippocampal neurogenesis improved resilience to chronic stress. These findings highlight a novel peptide produced by astrocytes that regulates the early steps of adult neurogenesis, with an implication for mood disorders.

neuroscience↗

bioRxiv2022

Machine learning analysis of the T cell receptor repertoire identifies sequence features that predict self-reactivity

The T cell receptor (TCR) determines the specificity and affinity for both foreign and self-peptides presented by MHC. It is established that self-pMHC reactivity impacts T cell function, but it has been challenging to identify TCR sequence features that predict T cell fate. To discern patterns distinguishing TCRs from naive CD4+ T cells with low versus high self-pMHC reactivity, we used data from 42 mice to train a machine learning (ML) algorithm that predicts self-reactivity directly from TCR{beta} sequences. This approach revealed that n-nucleotide additions and acidic amino acids weaken self-reactivity. We tested our ML predictions of TCR{beta} sequence self-reactivity using retrogenic mice. Extrapolating our analyses to independent datasets, we found high predicted self-reactivity for regulatory CD4+ T cells and low predicted self-reactivity for T cells responding to chronic infection. Our analyses suggest a potential trade-off between repertoire diversity and self-reactivity intrinsic to the architecture of a TCR repertoire.

immunology↗

bioRxiv2022

Comprehensive multi-omics characterization of gut microbiome extracellular vesicles reveals a connection to gut-brain axis signaling

Microbiota-gut-brain axis is an evident pathway of host-microbiota crosstalk that is linked to multiple brain disorders. Microbiota released extracellular vesicles (MEVs) has emerged as a key player in intercellular signaling in host microbiome communications. However, their role in gutbrain axis signaling is poorly investigated. Here, we performed a deep multi-omics profiling of MEVs content generated ex vivo and from stool samples in order to get some insights on their role in gut-brain-axis signaling. Metabolomics profiling identified a wide array of metabolites embedded in MEVs, including lipids, carbohydrates, amino acids, vitamins, and organic acids. Interestingly, many neurotransmitter-related compounds were detected inside MEVs, including arachidonyl-dopamine (NADA), gabapentin, glutamate and N-acylethanolamines. Next, we aimed to identify commensal microbes with psychobiotic activity. We isolated 58 Bacteroides strains assigned to four genera, 11 species, and 4 new species based on 16S rDNA sequencing. We performed whole genome sequencing of 18 representative isolates, followed by a comparative analysis of the structure of polysaccharide utilization loci (PUL) and glutamate decarboxylase (GAD), a genetic system involved in GABA production. Quantifying GABA was done using competitive ELISA, wherein three isolates (B. finegoldii, B. faecis, and B. caccae) showed high GABA production (4.5-7 mM range) in supernatant whereas 2.2 to 4 uM GABA concentration was detected inside microvesicles extracted using ultracentrifugation. To test the biodistribution of MEVs from the gut to other parts of the body, CACO-2, RIN-14 B, and hCMEC/D3 cells showed a capacity to internalize labeled MEVs through an endocytic mechanism. Additionally, MEVs exhibited a dose dependent paracellular transport through CACO-2 intestinal cells and hCMEC/D3 brain endothelial cells. In vivo results showed biodistribution of MEVs to liver, stomach and spleen. Overall, our results reveal the capabilities of MEVs to cross the intestinal and blood brain barriers to deliver their cargoes of neuroactive molecules to the brain as a new signaling mechanism in microbiota-gut-brain axis communications.

microbiology↗

bioRxiv2021

A Segmentation-Free Machine Learning Architecture for Immune Landscape Phenotyping in Solid Tumors by Multichannel Imaging

Tissue specimens taken from primary tumors or metastases contain important information for diagnosis and treat-ment of cancer patients. Multiplex imaging allows in situ visualization of heterogeneous cell populations, such as immune cells, in tissue samples. Most image processing pipelines first segment cell boundaries and then measure marker expression to assign cell phenotypes. In dense tissue environments, this segmentation-first approach can be inaccurate due to segmentation errors or overlapping cells. Here we introduce the machine learning pipeline "ImmuNet" that identifies positions and phenotypes of cells without segmenting them. ImmuNet is easy to train: human annotators only need to click on an immune cell and score its expression of each marker. This approach al-lowed us to annotate 34,458 cells. We show that ImmuNet consistently outperforms a state-of-the-art segmentation-based pipeline for multiplex immunohistochemistry analysis across tissue types, cell types and tissue densities, achieving error rates below 5-10% on challenging detection and phenotyping tasks. We externally validate Im-muNet results by comparing them to flow cytometric measurements from the same tissue. In summary, ImmuNet is an effective, simpler alternative to segmentation-based approaches when only cell positions and phenotypes, but not their shapes, are required for downstream analyses. Thus, ImmuNet helps researchers to analyze multiplex tissue images more easily and accurately.

immunology↗

bioRxiv2018

STAT6 mRNA and protein knockdown using multiple siRNA sequences inhibits proliferation and induces apoptosis of the human colon adenocarcinoma cell line, HT-29

The transcription factor STAT6 is strongly expressed in various tumours and is most highly expressed in malignant lymphomas and pancreatic, colorectal, prostate and breast cancers. STAT6 expression in colorectal cancer is associated with an increased malignancy, poor prognosis and poor survival rates. Colorectal cancer has an incidence of approximately 1,361,000 patients per annum worldwide and approximately 60% of those cancers show STAT6 expression. Techniques aimed at reducing or blocking STAT6 expression may be useful in treating colorectal cancers. Celixirs four proprietary STAT6 specific small interfering RNA (siRNA) sequences were tested in vitro using the human colon adenocarcinoma cell line, HT-29. The four sequences were introduced individually and in combination into HT-29 cells at different concentrations (10 to 200 nM). Decreases in STAT6 mRNA and protein levels were analysed to confirm the transfection was successful. STAT6 knockdown effects were measured by analysing cell proliferation and apoptosis. Results showed that 100nM siRNA concentration was the most effective and all four individual sequences knocked-down STAT6 mRNA and protein by more than 50%. Although all individual sequences were capable of significantly inhibiting cell proliferation, STAT6.1 and STAT6.4 were the best. STAT6 silencing also significantly induced late and total apoptotic events. In conclusion, these results demonstrate that STAT6 siRNA sequences are capable of inhibiting the proliferation, and inducing late apoptosis, of HT-29 colon cancer cells and, in some instances, halving the number of cancer cells. These experiments will be repeated using xenografts of STAT6-expressing colon cancer cells in immunocompromised mice and the STAT6 siRNA sequences will be tested in other cancers in which STAT6 is expressed. The STAT6 siRNA sequences therefore represent a potential treatment for the most serious colorectal cancers and a wide variety of STAT6-expressing cancers.

cancer biology↗