Yuge Ji

Abstract Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Here we present the integrated Human Lung Cell Atlas (HLCA), combining 49 datasets of the human respiratory system into a single atlas spanning over 2.4 million cells from 486 individuals. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. Leveraging the number and diversity of individuals in the HLCA, we identify gene modules that are associated with demographic covariates such as age, sex and body mass index, as well as gene modules changing expression along the proximal-to-distal axis of the bronchial tree. Mapping new data to the HLCA enables rapid data annotation and interpretation. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP1 + profibrotic monocyte-derived macrophages in COVID-19, pulmonary fibrosis and lung carcinoma. Overall, the HLCA serves as an example for the development and use of large-scale, cross-dataset organ atlases within the Human Cell Atlas.

Recent advances in multiplexed single-cell transcriptomics experiments facilitate the high-throughput study of drug and genetic perturbations. However, an exhaustive exploration of the combinatorial perturbation space is experimentally unfeasible. Therefore, computational methods are needed to predict, interpret, and prioritize perturbations. Here, we present the compositional perturbation autoencoder (CPA), which combines the interpretability of linear models with the flexibility of deep-learning approaches for single-cell response modeling. CPA learns to in silico predict transcriptional perturbation response at the single-cell level for unseen dosages, cell types, time points, and species. Using newly generated single-cell drug combination data, we validate that CPA can predict unseen drug combinations while outperforming baseline models. Additionally, the architecture's modularity enables incorporating the chemical representation of the drugs, allowing the prediction of cellular response to completely unseen drugs. Furthermore, CPA is also applicable to genetic combinatorial screens. We demonstrate this by imputing in silico 5,329 missing combinations (97.6% of all possibilities) in a single-cell Perturb-seq experiment with diverse genetic interactions. We envision CPA will facilitate efficient experimental design and hypothesis generation by enabling in silico response prediction at the single-cell level and thus accelerate therapeutic applications using single-cell technologies.

Abstract Phenotypic polarization of macrophages is regulated by a milieu of cues in the local tissue microenvironment. Currently, little is known about how the intrinsic regulators modulate proinflammatory (M1) versus prohealing (M2) macrophages activation. Here, it is observed that insulin‐like growth factor 2 messenger RNA (mRNA)‐binding protein 2 (IGF2BP2)‐deleted macrophages exhibit enhanced M1 phenotype and promote dextran sulfate sodium induced colitis development. However, the IGF2BP2 −/− macrophages are refractory to interleukin‐4 (IL‐4) induced activation and alleviate cockroach extract induced pulmonary allergic inflammation. Molecular studies indicate that IGF2BP2 switches M1 macrophages to M2 activation by targeting tuberous sclerosis 1 via an N6‐methyladenosine (m 6 A)‐dependent manner. Additionally, it is also shown a signal transducer and activators of transcription 6 (STAT6)‐high mobility group AT‐hook 2‐IGF2BP2‐peroxisome proliferator activated receptor‐ γ axis involves in M2 macrophages differentiation. These findings highlight a key role of IGF2BP2 in regulation of macrophages activation and imply a potential therapeutic target of macrophages in the inflammatory diseases.

Tumor metastasis is a hallmark of colorectal cancer (CRC), in which exosome plays a crucial role with its function in intercellular communication. Plasma exosomes were collected from healthy control (HC) donors, localized primary CRC and liver-metastatic CRC patients. We performed proximity barcoding assay (PBA) for single-exosome analysis, which enabled us to identify the alteration in exosome subpopulations associated with CRC progression. By in vitro and in vivo experiments, the biological impact of these subpopulations on cancer proliferation, migration, invasion, and metastasis was investigated. The potential application of exosomes as diagnostic biomarkers was evaluated in 2 independent validation cohorts by PBA. Twelve distinct exosome subpopulations were determined. We found 2 distinctly abundant subpopulations: one ITGB3-positive and the other ITGAM-positive. The ITGB3-positive cluster is rich in liver-metastatic CRC, compared to both HC group and primary CRC group. On the contrary, ITGAM-positive exosomes show a large-scale increase in plasma of HC group, compared to both primary CRC and metastatic CRC groups. Notably, both discovery cohort and validation cohort verified ITGB3+ exosomes as potential diagnostic biomarker. ITGB3+ exosomes promote proliferation, migration, and invasion capability of CRC. In contrast, ITGAM+ exosomes suppress CRC development. Moreover, we also provide evidence that one of the sources of ITGAM+ exosomes is macrophage. ITGB3+ exosomes and ITGAM+ exosomes are proven 2 potential diagnostic, prognostic, and therapeutic biomarkers for management of CRC.