Lanxiang Li

Recent developments of sequencing-based spatial transcriptomics (sST) have catalyzed important advancements by facilitating transcriptome-scale spatial gene expression measurement. Despite this progress, efforts to comprehensively benchmark different platforms are currently lacking. The extant variability across technologies and datasets poses challenges in formulating standardized evaluation metrics. In this study, we established a collection of reference tissues and regions characterized by well-defined histological architectures, and used them to generate data to compare 11 sST methods. We highlighted molecular diffusion as a variable parameter across different methods and tissues, significantly affecting the effective resolutions. Furthermore, we observed that spatial transcriptomic data demonstrate unique attributes beyond merely adding a spatial axis to single-cell data, including an enhanced ability to capture patterned rare cell states along with specific markers, albeit being influenced by multiple factors including sequencing depth and resolution. Our study assists biologists in sST platform selection, and helps foster a consensus on evaluation standards and establish a framework for future benchmarking efforts that can be used as a gold standard for the development and benchmarking of computational tools for spatial transcriptomic analysis.

BACKGROUND: There is a high prevalence of anemia among people living with HIV in Guangxi, China. Therefore, we investigated anemia and opportunistic infections in hospitalized people living with HIV and explored the risk factors related to anemia in people living with HIV to actively prevent anemia in people living with HIV. METHODS: test was used to compare the prevalence between the anemic and non-anemic groups. The logistic regression analysis was applied to exclude confounding factors and identify factors related to anemia. RESULTS: Among 5645 patients with HIV, 1525 (27.02%) had anemia. The overall prevalence of mild, moderate, and severe anemia was 4.66%, 14.08%, and 8.27%, respectively. The factors significantly related to increased risk of anemia were CD4 count < 50 cells/µl (aOR = 2.221, 95% CI = [1.775, 2.779]), CD4 count 50-199 cells/µl (aOR = 1.659, 95% CI = [1.327, 2. 073]), female (aOR = 1.644, 95% CI = [1.436, 1.881]) co-infected with HCV (aOR = 1.465, 95% CI = [1.071, 2.002]), PM (aOR = 2.356, 95% CI = [1.950, 2.849]), or TB (aOR = 1.198, 95% CI = [1.053, 1.365]). CONCLUSIONS: Within Guangxi of China, 27.02% of hospitalized people living with HIV presented with anemia. Most patients with anemia were in the mild to moderate stage. The low CD4 count, female gender, and concomitant infection with Penicillium marneffei, Hepatitis C virus, or Tuberculosis were independent correlates of anemia. Thus, these findings would be helpful to clinicians in preventing and intervening in anemia in people living with HIV.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the cause of the ongoing coronavirus disease 2019 (COVID-19) pandemic. Understanding the influence of mutations in the SARS-CoV-2 gene on clinical outcomes is critical for treatment and prevention. Here, we analyzed all high-coverage complete SARS-CoV-2 sequences from GISAID database from January 1, 2020, to January 1, 2021, to mine the mutation hotspots associated with clinical outcome and developed a model to predict the clinical outcome in different epidemic strains. Exploring the cause of mutation based on RNA-dependent RNA polymerase (RdRp) and RNA-editing enzyme, mutation was more likely to occur in severe and mild cases than in asymptomatic cases, especially A > G, C > T, and G > A mutations. The mutations associated with asymptomatic outcome were mainly in open reading frame 1ab (ORF1ab) and N genes; especially R6997P and V30L mutations occurred together and were correlated with asymptomatic outcome with high prevalence. D614G, Q57H, and S194L mutations were correlated with mild and severe outcome with high prevalence. Interestingly, the single-nucleotide variant (SNV) frequency was higher with high percentage of nt14408 mutation in RdRp in severe cases. The expression of ADAR and APOBEC was associated with clinical outcome. The model has shown that the asymptomatic percentage has increased over time, while there is high symptomatic percentage in Alpha, Beta, and Gamma. These findings suggest that mutation in the SARS-CoV-2 genome may have a direct association with clinical outcomes and pandemic. Our result and model are helpful to predict the prevalence of epidemic strains and to further study the mechanism of mutation causing severe disease.

Abstract Predicting single-cell transcriptional responses to perturbations is central to dissecting gene regulation and accelerating therapeutic design, yet the field lacks a rigorous, task-spanning assessment of model behavior. We present a large-scale benchmark of 12 representative methods and 3 baselines across 25 datasets spanning diverse perturbation modalities and species, including two new primary immune-cell drug-response resources. We evaluated three core tasks—generalization to unseen single-gene perturbations, prediction of combinatorial interactions, and transfer across cell types—using 24 metrics covering expression-level accuracy, relative changes, differential expression recovery, and distributional similarity. Across tasks, performance depended strongly on perturbation effect size and evaluation perspective: expression-level agreement was highest for small-effect perturbations resembling controls, whereas delta- and DE-based metrics improved with larger effects, providing clearer signals. Models shared a conservative bias, with fine-tuned foundation models compressing variance and underestimating synergistic effects in combinations. PerturbNet showed superior recovery of DE signatures in Tasks 1 and 2, while no method consistently generalized across cell types in Task 3, where dataset heterogeneity dominated outcomes. This benchmark establishes current methodological limits, clarifies when metrics diverge, and provides a foundation for developing virtual-cell models that more faithfully capture heterogeneous perturbation responses.