Noorsher Ahmed

All Publications

• A comprehensive benchmark of sequence-based subcellular localization predictors for human proteins. Nature methods Wefers, Z., Gupta, A., Ahmed, N., Zhang, X., Lundberg, E. 2026; 23 (7): 1458-1469

  Abstract

  Computational sequence-based predictors of protein localization have the potential to accelerate the discovery of protein functions and interactions, thereby advancing our understanding of human biology and disease. While many methods have been proposed, evaluations remain limited by small test sets, coarse-grained cellular compartment labels and single-label classification, despite the fact that nearly half of human proteins localize to multiple compartments. Here we integrate annotations from major protein databases to construct a highly validated, twofold larger benchmark test set of 3,814 human proteins. Using this dataset, we systematically evaluate existing sequence-based predictors and compare combinations of protein language models and aggregation strategies. We find that current models underperform on fine-grained compartments, multilocalizing proteins and pathogenic variants known to mislocalize. Our results reveal fundamental limitations of existing approaches and underscore the need for improved models, standardized benchmark datasets and more rigorous evaluation in subcellular localization prediction.

  View details for DOI 10.1038/s41592-026-03142-6

  View details for PubMedID 42410059

  View details for PubMedCentralID 2859129

• Bento: a toolkit for subcellular analysis of spatial transcriptomics data. Genome biology Mah, C. K., Ahmed, N., Lopez, N. A., Lam, D. C., Pong, A., Monell, A., Kern, C., Han, Y., Prasad, G., Cesnik, A. J., Lundberg, E., Zhu, Q., Carter, H., Yeo, G. W. 2024; 25 (1): 82

  Abstract

  The spatial organization of molecules in a cell is essential for their functions. While current methods focus on discerning tissue architecture, cell-cell interactions, and spatial expression patterns, they are limited to the multicellular scale. We present Bento, a Python toolkit that takes advantage of single-molecule information to enable spatial analysis at the subcellular scale. Bento ingests molecular coordinates and segmentation boundaries to perform three analyses: defining subcellular domains, annotating localization patterns, and quantifying gene-gene colocalization. We demonstrate MERFISH, seqFISH+, Molecular Cartography, and Xenium datasets. Bento is part of the open-source Scverse ecosystem, enabling integration with other single-cell analysis tools.

  View details for DOI 10.1186/s13059-024-03217-7

  View details for PubMedID 38566187

• Illuminating RNA biology through imaging NATURE CELL BIOLOGY Le, P., Ahmed, N., Yeo, G. W. 2022; 24 (6): 815-824

  Abstract

  RNA processing plays a central role in accurately transmitting genetic information into functional RNA and protein regulators. To fully appreciate the RNA life-cycle, tools to observe RNA with high spatial and temporal resolution are critical. Here we review recent advances in RNA imaging and highlight how they will propel the field of RNA biology. We discuss current trends in RNA imaging and their potential to elucidate unanswered questions in RNA biology.

  View details for DOI 10.1038/s41556-022-00933-9

  View details for Web of Science ID 000810380000001

  View details for PubMedID 35697782

  View details for PubMedCentralID PMC11132331

• Pooled CRISPR screens with imaging on microraft arrays reveals stress granule-regulatory factors NATURE METHODS Wheeler, E. C., Vu, A. Q., Einstein, J. M., DiSalvo, M., Ahmed, N., Van Nostrand, E. L., Shishkin, A. A., Jin, W., Allbritton, N. L., Yee, G. W. 2020; 17 (6): 636-+

  Abstract

  Genetic screens using pooled CRISPR-based approaches are scalable and inexpensive, but restricted to standard readouts, including survival, proliferation and sortable markers. However, many biologically relevant cell states involve cellular and subcellular changes that are only accessible by microscopic visualization, and are currently impossible to screen with pooled methods. Here we combine pooled CRISPR-Cas9 screening with microraft array technology and high-content imaging to screen image-based phenotypes (CRaft-ID; CRISPR-based microRaft followed by guide RNA identification). By isolating microrafts that contain genetic clones harboring individual guide RNAs (gRNA), we identify RNA-binding proteins (RBPs) that influence the formation of stress granules, the punctate protein-RNA assemblies that form during stress. To automate hit identification, we developed a machine-learning model trained on nuclear morphology to remove unhealthy cells or imaging artifacts. In doing so, we identified and validated previously uncharacterized RBPs that modulate stress granule abundance, highlighting the applicability of our approach to facilitate image-based pooled CRISPR screens.

  View details for DOI 10.1038/s41592-020-0826-8

  View details for Web of Science ID 000531779800001

  View details for PubMedID 32393832

  View details for PubMedCentralID PMC7357298

• Bulk double emulsification for flow cytometric analysis of microfluidic droplets ANALYST Sukovich, D. J., Kim, S. C., Ahmed, N., Abate, A. R. 2017; 142 (24): 4618-4622

  Abstract

  Droplet microfluidics is valuable for applications in chemistry and biology, but generates massive numbers of droplets that must be analyzed and sorted. Here, we describe a simple approach to bulk double emulsify microfluidic emulsions for analysis and sorting with commercial flow cytometers. We illustrate the method by using it to identify droplets based on nucleic acid content. Though simple, our method provides a general approach for analyzing and sorting microfluidic droplets without custom microfluidic double emulsifiers or sorters.

  View details for DOI 10.1039/c7an01695f

  View details for Web of Science ID 000416993900004

  View details for PubMedID 29131209

  View details for PubMedCentralID PMC5997486

• Single-cell genome sequencing at ultra-high-throughput with microfluidic droplet barcoding NATURE BIOTECHNOLOGY Lan, F., Demaree, B., Ahmed, N., Abate, A. R. 2017; 35 (7): 640-+

  Abstract

  The application of single-cell genome sequencing to large cell populations has been hindered by technical challenges in isolating single cells during genome preparation. Here we present single-cell genomic sequencing (SiC-seq), which uses droplet microfluidics to isolate, fragment, and barcode the genomes of single cells, followed by Illumina sequencing of pooled DNA. We demonstrate ultra-high-throughput sequencing of >50,000 cells per run in a synthetic community of Gram-negative and Gram-positive bacteria and fungi. The sequenced genomes can be sorted in silico based on characteristic sequences. We use this approach to analyze the distributions of antibiotic-resistance genes, virulence factors, and phage sequences in microbial communities from an environmental sample. The ability to routinely sequence large populations of single cells will enable the de-convolution of genetic heterogeneity in diverse cell populations.

  View details for DOI 10.1038/nbt.3880

  View details for Web of Science ID 000405310300015

  View details for PubMedID 28553940

  View details for PubMedCentralID PMC5531050

• Operation of Droplet-Microfluidic Devices with a Lab Centrifuge MICROMACHINES Ahmed, N., Sukovich, D., Abate, A. R. 2016; 7 (9)

  Abstract

  Microfluidic devices are valuable for a variety of biotechnology applications, such as synthesizing biochemical libraries, screening enzymes, and analyzing single cells. However, normally, the devices are controlled using specialized pumps, which require expert knowledge to operate. Here, we demonstrate operation of poly(dimethylsiloxane) devices without pumps. We build a scaffold that holds the device and reagents to be infused in a format that can be inserted into a 50 mL falcon tube and spun in a common lab centrifuge. By controlling the device design and centrifuge spin speed, we infuse the reagents at controlled flow rates. We demonstrate the encapsulation and culture of clonal colonies of red and green Escherichia coli in droplets seeded from single cells.

  View details for DOI 10.3390/mi7090161

  View details for Web of Science ID 000385483000016

  View details for PubMedID 30404331

  View details for PubMedCentralID PMC6190000