Publications

2026

1. 

   Single-cell and spatial profiling highlights TB-induced myofibroblasts as drivers of lung pathology

   Nuo Liu*, Ian M. Mbano*, Marc H. Wadsworth, Mark J. Chambers, Thabo Mpotje, Osaretin E. Asowata, Sarah K. Nyquist, Kievershen Nargan, Duran Ramsuran, Farina Karim, Travis K. Hughes, Joshua D. Bromley, Robert Krause, Threnesan Naidoo, Liku B. Tezera, Michaela T. Reichmann, Sharie Keanne Ganchua, Henrik N. Kløverpris, Kaylesh J. Dullabh, Rajhmun Madansein, Sergio Triana, Adrie J.C. Steyn, Bonnie Berger, Mohlopheni J. Marakalala, Gabriele Pollara, Sarah M. Fortune, JoAnne L. Flynn, Paul T. Elkington , Alex K. Shalek, and Alasdair Leslie

   Journal of Experimental Medicine, Jan 2026

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   Tuberculosis (TB) typically causes lung destruction and fibrosis, leading to approximately 1.3 million deaths annually. The cellular drivers of human TB immunopathology remain poorly defined. We performed single-cell RNA sequencing and spatial transcriptomics on lung tissues from TB-infected and TB-negative individuals, identifying 30 distinct immune, parenchymal, and stromal cell subsets. Several were linked to TB pathology and corroborated through immunohistochemistry, flow cytometry, and independent datasets.

2024

1. 

   Scalable, compressed phenotypic screening using pooled perturbations

   Nuo Liu*, Walaa E. Kattan*, Benjamin E. Mead*, Conner Kummerlowe*, Thomas Cheng, Sarah Ingabire, Jaime H. Cheah, Christian K. Soule, Anita Vrcic, Jane K. McIninch, Sergio Triana, Manuel Guzman, Tyler T. Dao, Joshua M. Peters, Kristen E. Lowder, Lorin Crawford, Ava P. Amini, Paul C. Blainey, William C. Hahn, Brian Cleary, Bryan Bryson, Peter S. Winter, Srivatsan Raghavan , and Alex K. Shalek

   Nature Biotechnology, Oct 2024

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   High-throughput phenotypic screens using biochemical perturbations and high-content readouts are constrained by limitations of scale. To address this, we establish a method of pooling exogenous perturbations followed by computational deconvolution to reduce required sample size, labor and cost. We demonstrate the increased efficiency of compressed experimental designs compared to conventional approaches through benchmarking with a bioactive small-molecule library and a high-content imaging readout. We then apply compressed screening in two biological discovery campaigns. In the first, we use early-passage pancreatic cancer organoids to map transcriptional responses to a library of recombinant tumor microenvironment protein ligands, uncovering reproducible phenotypic shifts induced by specific ligands distinct from canonical reference signatures and correlated with clinical outcome. In the second, we identify the pleotropic modulatory effects of a chemical compound library with known mechanisms of action on primary human peripheral blood mononuclear cell immune responses. In sum, our approach empowers phenotypic screens with information-rich readouts to advance drug discovery efforts and basic biological inquiry.

2023

1. 

   xenoGI 3: using the DTLOR model to reconstruct the evolution of gene families in clades of microbes

   Nuo Liu, Tonatiuh A. Gonzalez, Jacob Fischer, Chan Hong, Michelle Johnson, Ross Mawhorter, Fabrizia Mugnatto, Rachael Soh, Shifa Somji, Joseph S. Wirth, Ran Libeskind-Hadas, and Eliot C. Bush

   BMC Bioinformatics, Jul 2023

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   To understand genome evolution in a group of microbes, we need to know the timing of events such as duplications, deletions and horizontal transfers. A common approach is to perform a gene-tree / species-tree reconciliation. While a number of software packages perform this type of analysis, none are geared toward a complete reconstruction for all families in an entire clade. Here we describe an update to the xenoGI software package which allows users to perform such an analysis using the newly developed DTLOR (duplication-transfer-loss-origin-rearrangement) reconciliation model starting from genome sequences as input.

2021

1. 

   Microenvironment drives cell state, plasticity, and drug response in pancreatic cancer

   Srivatsan Raghavan, Peter S. Winter, Andrew W. Navia, Hannah L. Williams, Alan DenAdel, Kristen E. Lowder, Jennyfer Galvez-Reyes, Radha L. Kalekar, Nolawit Mulugeta, Kevin S. Kapner, Manisha S. Raghavan, Ashir A. Borah, Nuo Liu, Sara A. Väyrynen, Andressa Dias Costa, Raymond W.S. Ng, Junning Wang, Emma K. Hill, Dorisanne Y. Ragon, Lauren K. Brais, Alex M. Jaeger, Liam F. Spurr, Yvonne Y. Li, Andrew D. Cherniack, Matthew A. Booker, Elizabeth F. Cohen, Michael Y. Tolstorukov, Isaac Wakiro, Asaf Rotem, Bruce E. Johnson, James M. McFarland, Ewa T. Sicinska, Tyler E. Jacks, Ryan J. Sullivan, Geoffrey I. Shapiro, Thomas E. Clancy, Kimberly Perez, Douglas A. Rubinson, Kimmie Ng, James M. Cleary, Lorin Crawford, Scott R. Manalis, Jonathan A. Nowak, Brian M. Wolpin, William C. Hahn, Andrew J. Aguirre , and Alex K. Shalek

   Cell, Dec 2021

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   Summary Prognostically relevant RNA expression states exist in pancreatic ductal adenocarcinoma (PDAC), but our understanding of their drivers, stability, and relationship to therapeutic response is limited. To examine these attributes systematically, we profiled metastatic biopsies and matched organoid models at single-cell resolution. In vivo, we identify a new intermediate PDAC transcriptional cell state and uncover distinct site- and state-specific tumor microenvironments (TMEs). Benchmarking models against this reference map, we reveal strong culture-specific biases in cancer cell transcriptional state representation driven by altered TME signals. We restore expression state heterogeneity by adding back in vivo-relevant factors and show plasticity in culture models. Further, we prove that non-genetic modulation of cell state can strongly influence drug responses, uncovering state-specific vulnerabilities. This work provides a broadly applicable framework for aligning cell states across in vivo and ex vivo settings, identifying drivers of transcriptional plasticity and manipulating cell state to target associated vulnerabilities.

2. Multiple Optimal Reconciliations Under the Duplication-Loss-Coalescence Model

   Du, Haoxing,  Ong, Yi Sheng,  Knittel, Marina,  Mawhorter, Ross,  Liu, Nuo,  Gross, Gianluca,  Tojo, Reiko,  Libeskind-Hadas, Ran, and  Wu, Yi-Chieh

   IEEE/ACM Transactions on Computational Biology and Bioinformatics, Dec 2021

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3. Maximum parsimony reconciliation in the DTLOR model

   Jingyi Liu, Ross Mawhorter, Nuo Liu, Santi Santichaivekin, Eliot Bush, and Ran Libeskind-Hadas

   BMC Bioinformatics, Aug 2021

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   Analyses of microbial evolution often use reconciliation methods. However, the standard duplication-transfer-loss (DTL) model does not account for the fact that species trees are often not fully sampled and thus, from the perspective of reconciliation, a gene family may enter the species tree from the outside. Moreover, within the genome, genes are often rearranged, causing them to move to new syntenic regions.

2020

1. An Integer Linear Programming Solution for the Most Parsimonious Reconciliation Problem under the Duplication-Loss-Coalescence Model

   Morgan Carothers, Joseph Gardi, Gianluca Gross, Tatsuki Kuze, Nuo Liu, Fiona Plunkett, Julia Qian, and Yi-Chieh Wu

   In Proceedings of the 11th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics , Aug 2020

   event-place: Virtual Event, USA

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   Given a gene tree, a species tree, and an association between their leaves, the maximum parsimony reconciliation (MPR) problem seeks to find a mapping of the gene tree to the species tree that explains their incongruity using a biological model of evolutionary events. Unfortunately, when simultaneously accounting for gene duplication, gene loss, and coalescence, the MPR problem is NP-hard. While an exact algorithm exists, it can be problematic to use in practice due to time and memory requirements. In this work, we present an integer linear programming (ILP) formulation for solving the MPR problem when considering duplications, losses, and coalescence. Our experimental results on a simulated data set of 12 Drosophila species shows that our new algorithm is both accurate and scalable. Furthermore, in contrast to the existing exact algorithm, our formulation allows users to limit the maximum runtime and thus trade-off accuracy and scalability, making it an attractive choice for phylogenetic pipelines.

2019

1. 

   Inferring Pareto-optimal reconciliations across multiple event costs under the duplication-loss-coalescence model

   Ross Mawhorter, Nuo Liu, Ran Libeskind-Hadas, and Yi-Chieh Wu

   BMC Bioinformatics, Dec 2019

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   Reconciliation methods are widely used to explain incongruence between a gene tree and species tree. However, the common approach of inferring maximum parsimony reconciliations (MPRs) relies on user-defined costs for each type of event, which can be difficult to estimate. Prior work has explored the relationship between event costs and maximum parsimony reconciliations in the duplication-loss and duplication-transfer-loss models, but no studies have addressed this relationship in the more complicated duplication-loss-coalescence model.