Temporal recording of mammalian development and precancer.

Temporal ordering of cellular events offers fundamental insights into biological phenomena. Although this is traditionally achieved through continuous direct observations1,2, an alternative solution leverages irreversible genetic changes, such as naturally occurring mutations, to create indelible marks that enables retrospective temporal ordering3-5. Using a multipurpose, single-cell CRISPR platform, we developed a molecular clock approach to record the timing of cellular events and clonality in vivo, with incorporation of cell state and lineage information. Using this approach, we uncovered precise timing of tissue-specific cell expansion during mouse embryonic development, unconventional developmental relationships between cell types and new epithelial progenitor states by their unique genetic histories. Analysis of mouse adenomas, coupled to multiomic and single-cell profiling of human precancers, with clonal analysis of 418 human polyps, demonstrated the occurrence of polyclonal initiation in 15-30% of colonic precancers, showing their origins from multiple normal founders. Our study presents a multimodal framework that lays the foundation for in vivo recording, integrating synthetic or natural indelible genetic changes with single-cell analyses, to explore the origins and timing of development and tumorigenesis in mammalian systems.

Stem cell activity-coupled suppression of endogenous retrovirus governs adult tissue regeneration.

Mammalian retrotransposons constitute 40% of the genome. During tissue regeneration, adult stem cells coordinately repress retrotransposons and activate lineage genes, but how this coordination is controlled is poorly understood. Here, we observed that dynamic expression of histone methyltransferase SETDB1 (a retrotransposon repressor) closely mirrors stem cell activities in murine skin. SETDB1 ablation leads to the reactivation of endogenous retroviruses (ERVs, a type of retrotransposon) and the assembly of viral-like particles, resulting in hair loss and stem cell exhaustion that is reversible by antiviral drugs. Mechanistically, at least two molecularly and spatially distinct pathways are responsible: antiviral defense mediated by hair follicle stem cells and progenitors and antiviral-independent response due to replication stress in transient amplifying cells. ERV reactivation is promoted by DNA demethylase ten-eleven translocation (TET)-mediated hydroxymethylation and recapitulated by ablating cell fate transcription factors. Together, we demonstrated ERV silencing is coupled with stem cell activity and essential for adult hair regeneration.

BATF is a major driver of NK cell epigenetic reprogramming and dysfunction in AML.

Myelodysplastic syndrome and acute myeloid leukemia (AML) belong to a continuous disease spectrum of myeloid malignancies with poor prognosis in the relapsed/refractory setting necessitating novel therapies. Natural killer (NK) cells from patients with myeloid malignancies display global dysfunction with impaired killing capacity, altered metabolism, and an exhausted phenotype at the single-cell transcriptomic and proteomic levels. In this study, we identified that this dysfunction was mediated through a cross-talk between NK cells and myeloid blasts necessitating cell-cell contact. NK cell dysfunction could be prevented by targeting the αvß-integrin/TGF-ß/SMAD pathway but, once established, was persistent because of profound epigenetic reprogramming. We identified BATF as a core transcription factor and the main mediator of this NK cell dysfunction in AML. Mechanistically, we found that BATF was directly regulated and induced by SMAD2/3 and, in turn, bound to key genes related to NK cell exhaustion, such as HAVCR2, LAG3, TIGIT, and CTLA4. BATF deletion enhanced NK cell function against AML in vitro and in vivo. Collectively, our findings reveal a previously unidentified mechanism of NK immune evasion in AML manifested by epigenetic rewiring and inactivation of NK cells by myeloid blasts. This work highlights the importance of using healthy allogeneic NK cells as an adoptive cell therapy to treat patients with myeloid malignancies combined with strategies aimed at preventing the dysfunction by targeting the TGF-ß pathway or BATF.

Interleukin-21 engineering enhances NK cell activity against glioblastoma via CEBPD.

Glioblastoma (GBM) is an aggressive brain cancer with limited therapeutic options. Natural killer (NK) cells are innate immune cells with strong anti-tumor activity and may offer a promising treatment strategy for GBM. We compared the anti-GBM activity of NK cells engineered to express interleukin (IL)-15 or IL-21. Using multiple in vivo models, IL-21 NK cells were superior to IL-15 NK cells both in terms of safety and long-term anti-tumor activity, with locoregionally administered IL-15 NK cells proving toxic and ineffective at tumor control. IL-21 NK cells displayed a unique chromatin accessibility signature, with CCAAT/enhancer-binding proteins (C/EBP), especially CEBPD, serving as key transcription factors regulating their enhanced function. Deletion of CEBPD resulted in loss of IL-21 NK cell potency while its overexpression increased NK cell long-term cytotoxicity and metabolic fitness. These results suggest that IL-21, through C/EBP transcription factors, drives epigenetic reprogramming of NK cells, enhancing their anti-tumor efficacy against GBM.

Forecasting acute kidney injury and resource utilization in ICU patients using longitudinal, multimodal models.

BACKGROUND: Advances in artificial intelligence (AI) have realized the potential of revolutionizing healthcare, such as predicting disease progression via longitudinal inspection of Electronic Health Records (EHRs) and lab tests from patients admitted to Intensive Care Units (ICU). Although substantial literature exists addressing broad subjects, including the prediction of mortality, length-of-stay, and readmission, studies focusing on forecasting Acute Kidney Injury (AKI), specifically dialysis anticipation like Continuous Renal Replacement Therapy (CRRT) are scarce. The technicality of how to implement AI remains elusive. OBJECTIVE: This study aims to elucidate the important factors and methods that are required to develop effective predictive models of AKI and CRRT for patients admitted to ICU, using EHRs in the Medical Information Mart for Intensive Care (MIMIC) database. METHODS: We conducted a comprehensive comparative analysis of established predictive models, considering both time-series measurements and clinical notes from MIMIC-IV databases. Subsequently, we proposed a novel multi-modal model which integrates embeddings of top-performing unimodal models, including Long Short-Term Memory (LSTM) and BioMedBERT, and leverages both unstructured clinical notes and structured time series measurements derived from EHRs to enable the early prediction of AKI and CRRT. RESULTS: Our multimodal model achieved a lead time of at least 12 h ahead of clinical manifestation, with an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.888 for AKI and 0.997 for CRRT, as well as an Area Under the Precision Recall Curve (AUPRC) of 0.727 for AKI and 0.840 for CRRT, respectively, which significantly outperformed the baseline models. Additionally, we performed a SHapley Additive exPlanation (SHAP) analysis using the expected gradients algorithm, which highlighted important, previously underappreciated predictive features for AKI and CRRT. CONCLUSION: Our study revealed the importance and the technicality of applying longitudinal, multimodal modeling to improve early prediction of AKI and CRRT, offering insights for timely interventions. The performance and interpretability of our model indicate its potential for further assessment towards clinical applications, to ultimately optimize AKI management and enhance patient outcomes.

Forecasting Acute Kidney Injury and Resource Utilization in ICU patients using longitudinal, multimodal models.

Background: Advances in artificial intelligence (AI) have realized the potential of revolutionizing healthcare, such as predicting disease progression via longitudinal inspection of Electronic Health Records (EHRs) and lab tests from patients admitted to Intensive Care Units (ICU). Although substantial literature exists addressing broad subjects, including the prediction of mortality, length-of-stay, and readmission, studies focusing on forecasting Acute Kidney Injury (AKI), specifically dialysis anticipation like Continuous Renal Replacement Therapy (CRRT) are scarce. The technicality of how to implement AI remains elusive. Objective: This study aims to elucidate the important factors and methods that are required to develop effective predictive models of AKI and CRRT for patients admitted to ICU, using EHRs in the Medical Information Mart for Intensive Care (MIMIC) database. Methods: We conducted a comprehensive comparative analysis of established predictive models, considering both time-series measurements and clinical notes from MIMIC-IV databases. Subsequently, we proposed a novel multi-modal model which integrates embeddings of top-performing unimodal models, including Long Short-Term Memory (LSTM) and BioMedBERT, and leverages both unstructured clinical notes and structured time series measurements derived from EHRs to enable the early prediction of AKI and CRRT. Results: Our multimodal model achieved a lead time of at least 12 hours ahead of clinical manifestation, with an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.888 for AKI and 0.997 for CRRT, as well as an Area Under the Precision Recall Curve (AUPRC) of 0.727 for AKI and 0.840 for CRRT, respectively, which significantly outperformed the baseline models. Additionally, we performed a SHapley Additive exPlanation (SHAP) analysis using the expected gradients algorithm, which highlighted important, previously underappreciated predictive features for AKI and CRRT. Conclusion: Our study revealed the importance and the technicality of applying longitudinal, multimodal modeling to improve early prediction of AKI and CRRT, offering insights for timely interventions. The performance and interpretability of our model indicate its potential for further assessment towards clinical applications, to ultimately optimize AKI management and enhance patient outcomes.

Label-aware distance mitigates temporal and spatial variability for clustering and visualization of single-cell gene expression data.

Clustering and visualization are essential parts of single-cell gene expression data analysis. The Euclidean distance used in most distance-based methods is not optimal. The batch effect, i.e., the variability among samples gathered from different times, tissues, and patients, introduces large between-group distance and obscures the true identities of cells. To solve this problem, we introduce Label-Aware Distance (LAD), a metric using temporal/spatial locality of the batch effect to control for such factors. We validate LAD on simulated data as well as apply it to a mouse retina development dataset and a lung dataset. We also found the utility of our approach in understanding the progression of the Coronavirus Disease 2019 (COVID-19). LAD provides better cell embedding than state-of-the-art batch correction methods on longitudinal datasets. It can be used in distance-based clustering and visualization methods to combine the power of multiple samples to help make biological findings.

Safety, efficacy and determinants of response of allogeneic CD19-specific CAR-NK cells in CD19+ B cell tumors: a phase 1/2 trial.

There is a pressing need for allogeneic chimeric antigen receptor (CAR)-immune cell therapies that are safe, effective and affordable. We conducted a phase 1/2 trial of cord blood-derived natural killer (NK) cells expressing anti-CD19 chimeric antigen receptor and interleukin-15 (CAR19/IL-15) in 37 patients with CD19+ B cell malignancies. The primary objectives were safety and efficacy, defined as day 30 overall response (OR). Secondary objectives included day 100 response, progression-free survival, overall survival and CAR19/IL-15 NK cell persistence. No notable toxicities such as cytokine release syndrome, neurotoxicity or graft-versus-host disease were observed. The day 30 and day 100 OR rates were 48.6% for both. The 1-year overall survival and progression-free survival were 68% and 32%, respectively. Patients who achieved OR had higher levels and longer persistence of CAR-NK cells. Receiving CAR-NK cells from a cord blood unit (CBU) with nucleated red blood cells ≤ 8 × 107 and a collection-to-cryopreservation time ≤ 24 h was the most significant predictor for superior outcome. NK cells from these optimal CBUs were highly functional and enriched in effector-related genes. In contrast, NK cells from suboptimal CBUs had upregulation of inflammation, hypoxia and cellular stress programs. Finally, using multiple mouse models, we confirmed the superior antitumor activity of CAR/IL-15 NK cells from optimal CBUs in vivo. These findings uncover new features of CAR-NK cell biology and underscore the importance of donor selection for allogeneic cell therapies. ClinicalTrials.gov identifier: NCT03056339 .

Single-nucleotide variant calling in single-cell sequencing data with Monopogen.

Single-cell omics technologies enable molecular characterization of diverse cell types and states, but how the resulting transcriptional and epigenetic profiles depend on the cell's genetic background remains understudied. We describe Monopogen, a computational tool to detect single-nucleotide variants (SNVs) from single-cell sequencing data. Monopogen leverages linkage disequilibrium from external reference panels to identify germline SNVs and detects putative somatic SNVs using allele cosegregating patterns at the cell population level. It can identify 100 K to 3 M germline SNVs achieving a genotyping accuracy of 95%, together with hundreds of putative somatic SNVs. Monopogen-derived genotypes enable global and local ancestry inference and identification of admixed samples. It identifies variants associated with cardiomyocyte metabolic levels and epigenomic programs. It also improves putative somatic SNV detection that enables clonal lineage tracing in primary human clonal hematopoiesis. Monopogen brings together population genetics, cell lineage tracing and single-cell omics to uncover genetic determinants of cellular processes.

Batch-Corrected Distance Mitigates Temporal and Spatial Variability for Clustering and Visualization of Single-Cell Gene Expression Data.

Clustering and visualization are essential parts of single-cell gene expression data analysis. The Euclidean distance used in most distance-based methods is not optimal. The batch effect, i.e., the variability among samples gathered from different times, tissues, and patients, introduces large between-group distance and obscures the true identities of cells. To solve this problem, we introduce Batch-Corrected Distance (BCD), a metric using temporal/spatial locality of the batch effect to control for such factors. We validate BCD on simulated data as well as applied it to a mouse retina development dataset and a lung dataset. We also found the utility of our approach in understanding the progression of the Coronavirus Disease 2019 (COVID-19). BCD achieves more accurate clusters and better visualizations than state-of-the-art batch correction methods on longitudinal datasets. BCD can be directly integrated with most clustering and visualization methods to enable more scientific findings.

Loss of metabolic fitness drives tumor resistance after CAR-NK cell therapy and can be overcome by cytokine engineering.

Chimeric antigen receptor (CAR) engineering of natural killer (NK) cells is promising, with early-phase clinical studies showing encouraging responses. However, the transcriptional signatures that control the fate of CAR-NK cells after infusion and factors that influence tumor control remain poorly understood. We performed single-cell RNA sequencing and mass cytometry to study the heterogeneity of CAR-NK cells and their in vivo evolution after adoptive transfer, from the phase of tumor control to relapse. Using a preclinical model of noncurative lymphoma and samples from a responder and a nonresponder patient treated with CAR19/IL-15 NK cells, we observed the emergence of NK cell clusters with distinct patterns of activation, function, and metabolic signature associated with different phases of in vivo evolution and tumor control. Interaction with the highly metabolically active tumor resulted in loss of metabolic fitness in NK cells that could be partly overcome by incorporation of IL-15 in the CAR construct.

Novornabreak: Local Assembly for Novel Splice Junction and Fusion Transcript Detection from RNA-Seq Data.

We present novoRNABreak, a unified framework for cancer specific novel splice junction and fusion transcript detection in RNA-seq data obtained from human cancer samples. novoRNABreak is based on a local assembly model, which offers a tradeoff between the alignment-based and de novo whole transcriptome assembly (WTA) methods. This approach is accurate and sensitive in assembling novel junctions that are difficult to directly align or have multiple alignments. Additionally, it is more efficient due to the strategy that focuses on junctions rather than full length transcripts. The performance of novoRNABreak is demonstrated by a comprehensive set of experiments using synthetic data generated based on genome reference, as well as real RNA-seq data from breast cancer and prostate cancer samples. The results show that our tool has a better performance by fully utilizing unmapped reads and precisely identifying the junctions where short reads or small exons have multiple alignments. novoRNABreak is a fully-fledged program available on GitHub (https://github.com/KChen-lab/novoRNABreak).

Temporal recording of mammalian development and precancer.

Key to understanding many biological phenomena is knowing the temporal ordering of cellular events, which often require continuous direct observations [1, 2]. An alternative solution involves the utilization of irreversible genetic changes, such as naturally occurring mutations, to create indelible markers that enables retrospective temporal ordering [3-8]. Using NSC-seq, a newly designed and validated multi-purpose single-cell CRISPR platform, we developed a molecular clock approach to record the timing of cellular events and clonality in vivo , while incorporating assigned cell state and lineage information. Using this approach, we uncovered precise timing of tissue-specific cell expansion during murine embryonic development and identified new intestinal epithelial progenitor states by their unique genetic histories. NSC-seq analysis of murine adenomas and single-cell multi-omic profiling of human precancers as part of the Human Tumor Atlas Network (HTAN), including 116 scRNA-seq datasets and clonal analysis of 418 human polyps, demonstrated the occurrence of polyancestral initiation in 15-30% of colonic precancers, revealing their origins from multiple normal founders. Thus, our multimodal framework augments existing single-cell analyses and lays the foundation for in vivo multimodal recording, enabling the tracking of lineage and temporal events during development and tumorigenesis.

Direct CD32 T-cell cytotoxicity: implications for breast cancer prognosis and treatment.

The FcγRII (CD32) ligands are IgFc fragments and pentraxins. The existence of additional ligands is unknown. We engineered T cells with human chimeric receptors resulting from the fusion between CD32 extracellular portion and transmembrane CD8α linked to CD28/ζ chain intracellular moiety (CD32-CR). Transduced T cells recognized three breast cancer (BC) and one colon cancer cell line among 15 tested in the absence of targeting antibodies. Sensitive BC cell conjugation with CD32-CR T cells induced CD32 polarization and down-regulation, CD107a release, mutual elimination, and proinflammatory cytokine production unaffected by human IgGs but enhanced by cetuximab. CD32-CR T cells protected immunodeficient mice from subcutaneous growth of MDA-MB-468 BC cells. RNAseq analysis identified a 42 gene fingerprint predicting BC cell sensitivity and favorable outcomes in advanced BC. ICAM1 was a major regulator of CD32-CR T cell-mediated cytotoxicity. CD32-CR T cells may help identify cell surface CD32 ligand(s) and novel prognostically relevant transcriptomic signatures and develop innovative BC treatments.

KLF5 governs sphingolipid metabolism and barrier function of the skin.

Stem cells are fundamental units of tissue remodeling whose functions are dictated by lineage-specific transcription factors. Home to epidermal stem cells and their upward-stratifying progenies, skin relies on its secretory functions to form the outermost protective barrier, of which a transcriptional orchestrator has been elusive. KLF5 is a Krüppel-like transcription factor broadly involved in development and regeneration whose lineage specificity, if any, remains unclear. Here we report KLF5 specifically marks the epidermis, and its deletion leads to skin barrier dysfunction in vivo. Lipid envelopes and secretory lamellar bodies are defective in KLF5-deficient skin, accompanied by preferential loss of complex sphingolipids. KLF5 binds to and transcriptionally regulates genes encoding rate-limiting sphingolipid metabolism enzymes. Remarkably, skin barrier defects elicited by KLF5 ablation can be rescued by dietary interventions. Finally, we found that KLF5 is widely suppressed in human diseases with disrupted epidermal secretion, and its regulation of sphingolipid metabolism is conserved in human skin. Altogether, we established KLF5 as a disease-relevant transcription factor governing sphingolipid metabolism and barrier function in the skin, likely representing a long-sought secretory lineage-defining factor across tissue types.

Bi-order multimodal integration of single-cell data.

Integration of single-cell multiomics profiles generated by different single-cell technologies from the same biological sample is still challenging. Previous approaches based on shared features have only provided approximate solutions. Here, we present a novel mathematical solution named bi-order canonical correlation analysis (bi-CCA), which extends the widely used CCA approach to iteratively align the rows and the columns between data matrices. Bi-CCA is generally applicable to combinations of any two single-cell modalities. Validations using co-assayed ground truth data and application to a CAR-NK study and a fetal muscle atlas demonstrate its capability in generating accurate multimodal co-embeddings and discovering cellular identity.

Systematic decomposition of sequence determinants governing CRISPR/Cas9 specificity.

The specificity of CRISPR/Cas9 genome editing is largely determined by the sequences of guide RNA (gRNA) and the targeted DNA, yet the sequence-dependent rules underlying off-target effects are not fully understood. To systematically explore the sequence determinants governing CRISPR/Cas9 specificity, here we describe a dual-target system to measure the relative cleavage rate between off- and on-target sequences (off-on ratios) of 1902 gRNAs on 13,314 synthetic target sequences, and reveal a set of sequence rules involving 2 factors in off-targeting: 1) a guide-intrinsic mismatch tolerance (GMT) independent of the mismatch context; 2) an "epistasis-like" combinatorial effect of multiple mismatches, which are associated with the free-energy landscape in R-loop formation and are explainable by a multi-state kinetic model. These sequence rules lead to the development of MOFF, a model-based predictor of Cas9-mediated off-target effects. Moreover, the "epistasis-like" combinatorial effect suggests a strategy of allele-specific genome editing using mismatched guides. With the aid of MOFF prediction, this strategy significantly improves the selectivity and expands the application domain of Cas9-based allele-specific editing, as tested in a high-throughput allele-editing screen on 18 cancer hotspot mutations.

Sensei: how many samples to tell a change in cell type abundance?

Cellular heterogeneity underlies cancer evolution and metastasis. Advances in single-cell technologies such as single-cell RNA sequencing and mass cytometry have enabled interrogation of cell type-specific expression profiles and abundance across heterogeneous cancer samples obtained from clinical trials and preclinical studies. However, challenges remain in determining sample sizes needed for ascertaining changes in cell type abundances in a controlled study. To address this statistical challenge, we have developed a new approach, named Sensei, to determine the number of samples and the number of cells that are required to ascertain such changes between two groups of samples in single-cell studies. Sensei expands the t-test and models the cell abundances using a beta-binomial distribution. We evaluate the mathematical accuracy of Sensei and provide practical guidelines on over 20 cell types in over 30 cancer types based on knowledge acquired from the cancer cell atlas (TCGA) and prior single-cell studies. We provide a web application to enable user-friendly study design via https://kchen-lab.github.io/sensei/table_beta.html .

Generation of glucocorticoid-resistant SARS-CoV-2 T cells for adoptive cell therapy.

Adoptive cell therapy with virus-specific T cells has been used successfully to treat life-threatening viral infections, supporting application of this approach to coronavirus disease 2019 (COVID-19). We expand severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) T cells from the peripheral blood of COVID-19-recovered donors and non-exposed controls using different culture conditions. We observe that the choice of cytokines modulates the expansion, phenotype, and hierarchy of antigenic recognition by SARS-CoV-2 T cells. Culture with interleukin (IL)-2/4/7, but not under other cytokine-driven conditions, results in more than 1,000-fold expansion in SARS-CoV-2 T cells with a retained phenotype, function, and hierarchy of antigenic recognition compared with baseline (pre-expansion) samples. Expanded cytotoxic T lymphocytes (CTLs) are directed against structural SARS-CoV-2 proteins, including the receptor-binding domain of Spike. SARS-CoV-2 T cells cannot be expanded efficiently from the peripheral blood of non-exposed controls. Because corticosteroids are used for management of severe COVID-19, we propose an efficient strategy to inactivate the glucocorticoid receptor gene (NR3C1) in SARS-CoV-2 CTLs using CRISPR-Cas9 gene editing.