Biology subjects

Iskander, J.

Publications and source records attributed to Iskander, J..

4 recordsLinked to original sources

bioRxiv2026

cellNexus: Quality control, annotation, aggregation and analytical layers for the Human Cell Atlas data

Large-scale single-cell atlases such as the Human Cell Atlas have transformed our understanding of human biology. Yet, the lack of a robust framework that standardises quality control, expands cellular annotation, and adds normalisation and analytical layers, limits multi-study analyses and the usefulness of this resource. Here we present cellNexus, a comprehensive resource that enhances the Human Cell Atlas collection into analysis-ready data by linking quality control layers, metadata enrichment, expression normalisation, analysis and data aggregation. These enhancements enable robust large-scale statistical modelling across studies, exemplified here by a multi-tissue map of immune cell communication during ageing. All harmonised layers are accessible via a public web interface and with R and Python APIs. By providing continuous integration with CELLxGENE releases, cellNexus transforms large cell atlas corpora into an accessible, reproducible, interoperable foundation for large-scale biological discovery and the next generation of single-cell foundation models.

bioinformatics↗

bioRxiv2026

PartiNet is a dynamic adaptive neural network for high-performance particle picking in cryo-electron microscopy

Accurate, efficient and autonomous particle picking is a major bottleneck in high-resolution cryo-electron microscopy (cryo-EM). We introduce PartiNet, an Artificial Intelligence (AI)-based particle picker with size-agnostic detection and pre-trained models that eliminate manual parameter specification and dataset-specific training, pioneering dynamic neural network inference for single particle cryo-EM pipeline. Unlike static architectures, PartiNet employs a dynamic framework that adjusts network complexity in real-time based on perceived micrograph quality. This adaptive approach accelerates inference up to 7-fold compared to existing tools without sacrificing particle selection quality. Training on diverse protein datasets showed that PartiNet improves particle yields, enhances sampling of rare orientations, and is compatible with on-the-fly workflows. Comprehensive evaluation on benchmark datasets and validation on a new dataset of the chromatin remodeler MORC2 demonstrates superior precision and recall, with the ability to detect heterogeneous protein species, leading to more complete structural models and consistently higher-resolution reconstructions.

molecular biology↗

bioRxiv2025

ProteinDJ: a high-performance and modular protein design pipeline

Leveraging artificial intelligence and deep learning to generate proteins de novo (a.k.a. synthetic proteins) has unlocked new frontiers of protein design. Deep learning models trained on protein structures can generate novel protein designs that explore structural landscapes unseen by evolution. This approach enables the development of bespoke binders that target specific proteins and domains through new protein-protein interactions. However, successful binder generation can suffer from low in silico success rates, often requiring thousands of designs and hundreds of GPU hours to obtain enough hits for experimental testing. While workstation implementations are available for binder design, these are limited in both scalability and throughput. There is a lack of efficient open-source protein design pipelines for high-performance computing (HPC) systems that can maximise hardware resources and parallelise the workflow efficiently. Here, we present ProteinDJ--an implementation of a synthetic protein design workflow that is deployable on HPC systems using the Nextflow portable workflow management system and Apptainer containerisation. It parallelises the workload across both GPUs and CPUs, facilitating generation and testing of hundreds of designs per hour, accelerating the discovery process. ProteinDJ is designed to be modular and includes RoseTTAFold Diffusion (RFdiffusion) or BindCraft for fold generation, ProteinMPNN or Full-Atom MPNN (FAMPNN) for sequence design, and AlphaFold2 or Boltz-2 for prediction and validation of designs and binder-target interfaces, with supporting packages for structural evaluation of designs. ProteinDJ democratises protein binder design through its robust and user-friendly implementation and provides a framework for future protein design pipelines. ProteinDJ is freely available at https://github.com/PapenfussLab/proteindj.

bioinformatics↗

bioRxiv2023

Whole body cell map tracks tissue-specific immune cell accumulation and plasticity loss through ageing

Understanding tissue biologys heterogeneity is crucial for advancing precision medicine. Despite the centrality of the immune system in tissue homeostasis, a detailed and comprehensive map of immune cell distribution and interactions across human tissues and demographics remains elusive. To fill this gap, we harmonised data from 12,981 single-cell RNA sequencing samples and curated 29 million cells from 45 anatomical sites to create a comprehensive compositional and transcriptional healthy map of the healthy immune system. We used this resource and a novel multilevel modelling approach to track immune ageing and test differences across sex and ethnicity. We uncovered conserved and tissue-specific immune-ageing programs, resolved sex-dependent differential ageing and identified ethnic diversity in clinically critical immune checkpoints. This study provides a quantitative baseline of the immune system, facilitating advances in precision medicine. By sharing our immune map, we hope to catalyse further breakthroughs in cancer, infectious disease, immunology and precision medicine.

immunology↗