Heterogeneous Cell Populations Research Articles

Longitudinal changes in the transcriptionally active and intact HIV reservoir after starting ART during acute infection.

Even in antiretroviral therapy (ART)-suppressed human immunodeficiency virus (HIV)-infected individuals, there are heterogeneous populations of HIV-expressing cells exhibiting variable degrees of progression through blocks to HIV transcriptional initiation, elongation, completion, and splicing. These HIV-transcribing cells likely contribute to HIV-associated immune activation and inflammation as well as the viral rebound that occurs after stopping ART. However, it is unclear whether the blocks to HIV transcription are present before ART and how the timing and duration of ART may affect the clearance of cells expressing HIV transcripts that differ in their processivity and/or presence of mutations. To investigate these questions, we quantified different types of HIV transcripts and the corresponding HIV DNA regions/proviruses in longitudinal blood samples obtained before ART initiation (T1) and after 6 months (T2) and 1 year (T3) of ART in 16 individuals who initiated ART during acute HIV infection. Before ART, the pattern of HIV transcripts suggested blocks to elongation and splicing, and only ~10% of intact proviruses were transcribing intact HIV RNA. During the first 6 months of ART, we detected progressively greater reductions in initiated, 5'-elongated, mid-transcribed, completed, and multiply spliced HIV transcripts. Completed HIV RNA decayed faster than initiated or 5'-elongated HIV RNA, and intact HIV RNA tended to decay faster than defective HIV RNA. HIV DNA and RNA levels at T1-T3 correlated inversely with baseline CD4+ T-cell counts. Our findings suggest the existence of immune responses that act selectively to reduce HIV transcriptional completion and/or preferentially kill cells making completed or intact HIV RNA.IMPORTANCEEven in virologically suppressed HIV-infected individuals, expression of viral products from both intact and defective proviruses may contribute to HIV-associated immune activation and inflammation, which are thought to underlie the organ damage that persists despite suppressive ART. We investigated how the timing of ART initiation and the duration of ART affect the heterogeneous populations of HIV-transcribing cells, including a detailed characterization of the different HIV transcripts produced before ART and the rate at which they decay after ART initiation during acute HIV infection. Even during untreated infection, most cells (~90%) have blocks at some stage of transcription. Furthermore, different HIV transcripts decline at different rates on ART, with the fastest decay of cells making completed and intact HIV RNA. Our results suggest that intrinsic or extrinsic immune responses act selectively to either reduce particular stages of HIV transcription or cause selective killing of cells making particular HIV transcripts.

Volumetric trans-scale imaging of massive quantity of heterogeneous cell populations in centimeter-wide tissue and embryo.

We established a volumetric trans-scale imaging system with an ultra-large field-of-view (FOV) that enables simultaneous observation of millions of cellular dynamics in centimeter-wide three-dimensional (3D) tissues and embryos. Using a custom-made giant lens system with a magnification of ×2 and a numerical aperture (NA) of 0.25, and a CMOS camera with more than 100 megapixels, we built a trans-scale scope AMATERAS-2, and realized fluorescence imaging with a transverse spatial resolution of approximately 1.1 µm across an FOV of approximately 1.5×1.0 cm2. The 3D resolving capability was realized through a combination of optical and computational sectioning techniques tailored for our low-power imaging system. We applied the imaging technique to 1.2 cm-wide section of mouse brain, and successfully observed various regions of the brain with sub-cellular resolution in a single FOV. We also performed time-lapse imaging of a 1-cm-wide vascular network during quail embryo development for over 24 hr, visualizing the movement of over 4.0×105 vascular endothelial cells and quantitatively analyzing their dynamics. Our results demonstrate the potential of this technique in accelerating production of comprehensive reference maps of all cells in organisms and tissues, which contributes to understanding developmental processes, brain functions, and pathogenesis of disease, as well as high-throughput quality check of tissues used for transplantation medicine.

Volumetric trans-scale imaging of massive quantity of heterogeneous cell populations in centimeter-wide tissue and embryo

We established a volumetric trans-scale imaging system with an ultra-large field-of-view (FOV) that enables simultaneous observation of millions of cellular dynamics in centimeter-wide three-dimensional (3D) tissues and embryos. Using a custom-made giant lens system with a magnification of ×2 and a numerical aperture (NA) of 0.25, and a CMOS camera with more than 100 megapixels, we built a trans-scale scope AMATERAS-2, and realized fluorescence imaging with a transverse spatial resolution of approximately 1.1 µm across an FOV of approximately 1.5×1.0 cm2. The 3D resolving capability was realized through a combination of optical and computational sectioning techniques tailored for our low-power imaging system. We applied the imaging technique to 1.2 cm-wide section of mouse brain, and successfully observed various regions of the brain with sub-cellular resolution in a single FOV. We also performed time-lapse imaging of a 1-cm-wide vascular network during quail embryo development for over 24 hr, visualizing the movement of over 4.0×105 vascular endothelial cells and quantitatively analyzing their dynamics. Our results demonstrate the potential of this technique in accelerating production of comprehensive reference maps of all cells in organisms and tissues, which contributes to understanding developmental processes, brain functions, and pathogenesis of disease, as well as high-throughput quality check of tissues used for transplantation medicine.

Scavenger Receptor CD36 in Tumor-Associated Macrophages Promotes Cancer Progression by Dampening Type-I IFN Signaling.

Tumor-associated macrophages (TAM) are a heterogeneous population of myeloid cells that dictate the inflammatory tone of the tumor microenvironment. In this study, we unveiled a mechanism by which scavenger receptor cluster of differentiation 36 (CD36) suppresses TAM inflammatory states. CD36 was upregulated in TAMs and associated with immunosuppressive features, and myeloid-specific deletion of CD36 significantly reduced tumor growth. Moreover, CD36-deficient TAMs acquired inflammatory signatures including elevated type-I IFN (IFNI) production, mirroring the inverse correlation between CD36 and IFNI response observed in patients with cancer. IFNI, especially IFNβ, produced by CD36-deficient TAMs directly induced tumor cell quiescence and delayed tumor growth. Mechanistically, CD36 acted as a natural suppressor of IFNI signaling in macrophages through p38 activation downstream of oxidized lipid signaling. These findings establish CD36 as a critical regulator of TAM function and the tumor inflammatory microenvironment, providing additional rationale for pharmacologic inhibition of CD36 to rejuvenate antitumor immunity. Significance: CD36 in tumor-associated macrophages mediates immunosuppression and can be targeted as a therapeutic avenue for stimulating interferon production and increasing the efficacy of immunotherapy.

Ground-state pluripotent stem cells are characterized by Rac1-dependent Cadherin-enriched F-actin Complexes.

Pluripotent Stem Cells (PSCs) exhibit extraordinary differentiation potential and are thus highly valuable cellular model systems. However, while different PSC types corresponding to distinct stages of embryogenesis have been in common use, aspects of their cellular architecture and mechanobiology remain insufficiently understood. Here we investigated how the actin cytoskeleton is regulated in different pluripotency states. We observed a drastic reorganization during the transition from ground-state naïve mouse embryonic stem cells (mESCs) to converted prime epiblast stem cells (EpiSCs). mESCs are characterized by prominent actin-enriched cortical structures that contain cadherin-based cell-cell junctional components, despite not locating at cell-cell junctions. We termed these structures "Non-Junctional Cadherin Complexes (NJCC)" and showed that they are under low mechanical tension, depend on the ectodomain but not the cytoplasmic domain of E-cadherin, and exhibit minimal calcium dependence. Active Rac1 was identified as a negative regulator that promotes β-catenin dissociation and NJCC fragmentation. Our data suggests that NJCC may arise from the cis-association of E-cadherin ectodomain, with potential roles in ground-state pluripotency, and could serve as structural markers to distinguish heterogeneous population of pluripotent stem cells.

ScATAC-seq generates more accurate and complete regulatory maps than bulk ATAC-seq

Bulk ATAC-seq assays have been used to map and profile the chromatin accessibility of regulatory elements such as enhancers, promoters, and insulators. This has provided great insight into the regulation of gene expression in many cell types in a variety of organisms. To date, ATAC-seq has most often been used to provide an average evaluation of chromatin accessibility in populations of cells. The development of a single cell approach (scATAC-seq) assay enables researchers to evaluate chromatin accessibility in individual cells and identify sub-groups in mixed populations of cells. To investigate the full potential of single-cell epigenomic data, we have comprehensively compared the information derived from bulk ATAC-seq and scATAC-seq in populations of cells. We found that the chromatin architecture signal is the same using bulk ATAC-seq and scATAC-seq to analyse aliquots of the same cell population. However, scATAC-seq provides substantially higher data quality compared to bulk ATAC-seq improving the sensitivity to detect relatively weak, but functionally important ATAC-seq signals. Furthermore, we found that scATAC-seq identified differences in what was previously assumed to be a homogenous population of cells. Finally, we determined the number of cells required to generate aggregated open chromatin profiles from single cells and to identify biologically meaningful clusters after pseudo-bulking of data. This study illustrates the added value of using scATAC-seq rather than bulk ATAC-seq in evaluating both homogeneous and heterogeneous populations of cells. This paper provides a comprehensive guide on the benefits of using scATAC-seq data to study gene regulation.

Development of an attenuated glutamine synthetase (GS) selection system for the stable expression of tissue plasminogen activator in CHO-K1 cells

Chinese hamster ovary (CHO) cells represent the most common host system for the expression of high-quality recombinant proteins. The development of stable CHO cell lines used in industrial recombinant protein production often relies on dihydrofolate reductase (DHFR) and glutamine synthetase (GS) amplification systems. Conventional approaches to develop stable cell lines lead to heterogeneous cell populations. Consequently, it is desirable to adopt innovative strategies to increase the efficiency of clone selection to reduce the time and effort invested in the cell line development process. Attenuating the selection marker gene is an effective strategy for isolating high-producing cells. In this study, we evaluated the efficiency of an attenuated glutamine synthetase selection system for the expression of human tissue plasminogen activator (t-PA) in CHO cells. We introduced an AU-rich element (ARE) at the 3’UTR of the glutamine synthetase coding sequence and employed a weak promoter (mSV40) for the expression of this gene. Subsequently, we analyzed the effect of ARE on the GS RNA levels, and recombinant t-PA expression. Our results demonstrate that the use of ARE significantly enhances the detection of high expressing cells compared to the control. Additionally, the t-PA expression level in GS-ARE clones was approximately 900-fold greater than those without the ARE.

Detection of Cancer Stem Cells from Patient Samples.

The existence of cancer stem cells (CSCs) in various tumors has become increasingly clear in addition to their prominent role in therapy resistance, metastasis, and recurrence. For early diagnosis, disease progression monitoring, and targeting, there is a high demand for clinical-grade methods for quantitative measurement of CSCs from patient samples. Despite years of active research, standard measurement of CSCs has not yet reached clinical settings, especially in the case of solid tumors. This is because detecting this plastic heterogeneous population of cells is not straightforward. This review summarizes various techniques, highlighting their benefits and limitations in detecting CSCs from patient samples. In addition, methods designed to detect CSCs based on secreted and niche-associated signaling factors are reviewed. Spatial and single-cell methods for analyzing patient tumor tissues and noninvasive techniques such as liquid biopsy and in vivo imaging are discussed. Additionally, methods recently established in laboratories, preclinical studies, and clinical assays are covered. Finally, we discuss the characteristics of an ideal method as we look toward the future.

Single-cell transcriptome atlas of lamprey exploring Natterin- induced white adipose tissue browning

Lampreys are early jawless vertebrates that are the key to understanding the evolution of vertebrates. However, the lack of cytomic studies on multiple lamprey organs has hindered progress in this field. Therefore, the present study constructed a comprehensive cell atlas comprising 604,460 cells/nuclei and 70 cell types from 14 lamprey tissue samples. Comparison of cellular evolution across species revealed that most lamprey cell types are homologous to those in jawed vertebrates. We discovered acinar- and islet-like cell populations despite the lack of parenchymal organs in lampreys, providing evidence of pancreatic function in vertebrates. Furthermore, we investigated the heterogeneity of lamprey immune cell populations. Natterin was highly expressed in granulocytes, and NATTERIN was localized to the lipid droplets. Moreover, we developed a transgenic mouse model expressing Natterin to elucidate the role of NATTERIN in lipid metabolism, whereas the browning of white adipose tissue was induced. These findings elucidate vertebrate cellular evolution and advance our understanding of adipose tissue plasticity and metabolic regulation in lampreys.

The feasibility of combining immune checkpoint inhibitors with cancer-related fibroblasts for the treatment of tumors

In tumor therapy, immune checkpoint inhibitors (ICIs) are widely utilized. However, in most cases, patients with colorectal cancer and breast cancer have a low response rate to ICIs monotherapy. By directly or indirectly inhibiting T cells, Cancer-associated fibroblasts (CAFs) impact the immune response against tumors and immune cells' sensitivity to immune checkpoints. At present, the research on targeted CAFs for tumor treatment has become a hot topic. This paper analyzed the mechanism of immune resistance to ICIs in some tumor patients, found that CAFs promoted the formation of immune resistance in patients, and analyzed two ways of targeting CAFs to inhibit tumor progression and found that the drug GKT137831 could restore the sensitivity of patients to ICIs by inhibiting CAFs. The combined treatment of ICIs and CAFs provides a reference for the clinical treatment of tumors. However, CAFs are a highly heterogeneous cell population, so it is necessary to find more precise CAFs targeting drugs to enhance tumor immunogenicity and improve tumor sensitivity to ICIs through combination therapy.

Mapping the developmental trajectory of human astrocytes reveals divergence in glioblastoma.

Glioblastoma (GBM) is defined by heterogeneous and resilient cell populations that closely reflect neurodevelopmental cell types. Although it is clear that GBM echoes early and immature cell states, identifying the specific developmental programmes disrupted in these tumours has been hindered by a lack of high-resolution trajectories of glial and neuronal lineages. Here we delineate the course of human astrocyte maturation to uncover discrete developmental stages and attributes mirrored by GBM. We generated a transcriptomic and epigenomic map of human astrocyte maturation using cortical organoids maintained in culture for nearly 2 years. Through this approach, we chronicled a multiphase developmental process. Our time course of human astrocyte maturation includes a molecularly distinct intermediate period that serves as a lineage commitment checkpoint upstream of mature quiescence. This intermediate stage acts as a site of developmental deviation separating IDH-wild-type neoplastic astrocyte-lineage cells from quiescent astrocyte populations. Interestingly, IDH1-mutant tumour astrocyte-lineage cells are the exception to this developmental perturbation, where immature properties are suppressed as a result of D-2-hydroxyglutarate oncometabolite exposure. We propose that this defiance is a consequence of IDH1-mutant-associated epigenetic dysregulation, and we identified biased DNA hydroxymethylation (5hmC) in maturation genes as a possible mechanism. Together, this study illustrates a distinct cellular state aberration in GBM astrocyte-lineage cells and presents developmental targets for experimental and therapeutic exploration.

Intercellular signaling stabilizes single-cell level phenotypic transitions and accelerates the reestablishment of equilibrium of heterogeneous cancer cell populations.

Cancer cells within tumors exhibit a wide range of phenotypic states driven by non-genetic mechanisms in addition to extensively studied genetic alterations. Conversions among cancer cell states can result in intratumoral heterogeneity which contributes to metastasis and development of drug resistance. However, mechanisms underlying the initiation and/or maintenance of such phenotypic plasticity are poorly understood. In particular, the role of intercellular communications in phenotypic plasticity remains elusive. In this study, we employ a multiscale inference-based approach using single-cell RNA sequencing (scRNA-seq) data to explore how intercellular interactions influence phenotypic dynamics of cancer cells, particularly cancers undergoing epithelial-mesenchymal transition. Our inference approach reveals that signaling interactions between cancerous cells in small cell lung cancer (SCLC) result in seemingly contradictory behaviors, reinforcing the cellular phenotypes and maintaining population-level intratumoral heterogeneity. Additionally, we find a recurring signaling pattern across multiple types of cancer in which the mesenchymal-like subtypes utilize signals from other subtypes to reinforce its phenotype, further promoting the intratumoral heterogeneity. We use a mathematical model based on ordinary differential equations to show that inter-subtype communication accelerates the development of heterogeneous tumor populations. Our work highlights the critical role of intercellular signaling in sustaining intratumoral heterogeneity, and our approach of computational analysis of scRNA-seq data can infer inter- and intra-cellular signaling networks in a holistic manner.

Dendritic cells in triple-negative breast cancer: From pathophysiology to therapeutic applications.

Breast cancer is the second most commonly diagnosed cancer in women and the fifth leading cause of cancer-related deaths worldwide. It is a highly heterogeneous disease, consisting of multiple subtypes that vary significantly in clinical characteristics and survival outcomes. Triple-negative breast cancer (TNBC) is a particularly aggressive and challenging subtype of breast cancer. Several immunotherapeutic approaches have been tested in patients with TNBC to improve disease outcomes, including the administration of dendritic cell (DC)-based vaccines. DCs are a heterogeneous cell population that play a crucial role in bridging the innate and adaptive immune systems. Therefore, DCs have been increasingly used in cancer vaccines due to their ability to prime and boost antigen specific T-cell immune responses. This review aims to provide a comprehensive overview of TNBC, including potential targets and pharmacological strategies, as well as an overview of DCs and their relevance in TNBC. In addition, we review ongoing clinical trials and shed light on the evolving landscape of DC-based immunotherapy for TNBC.

Drug Delivery System Targeting Cancer-Associated Fibroblast for Improving Immunotherapy.

Cancer-associated fibroblasts (CAFs) are a heterogeneous population of non-malignant cells that play a crucial role in the tumor microenvironment, increasingly recognized as key contributors to cancer progression, metastasis, and treatment resistance. So, targeting CAFs has always been considered an important part of cancer immunotherapy. However, targeting CAFs to improve the efficacy of tumor therapy is currently a major challenge. Nanomaterials show their unique advantages in the whole process. At present, nanomaterials have achieved significant accomplishments in medical applications, particularly in the field of cancer-targeted therapy, showing enormous potential. It has been confirmed that nanomaterials can not only directly target CAFs, but also interact with the tumor microenvironment (TME) and immune cells to affect tumorigenesis. As for the cancer treatment, nanomaterials could enhance the therapeutic effect in many ways. Therefore, in this review, we first summarized the current understanding of the complex interactions between CAFs and TME, immune cells, and tumor cells. Next, we discussed common nanomaterials in modern medicine and their respective impacts on the TME, CAFs, and interactions with tumors. Finally, we focus on the application of nano drug delivery system targeting CAFs in cancer therapy.

Myeloid-derived Suppressor Cells and Multiple Sclerosis.

Myeloid-Derived Suppressor Cells (MDSCs) are a heterogeneous population of immature myeloid cells that play important roles in maintaining immune homeostasis and regulating immune responses. MDSCs can be divided into two main subsets based on their surface markers and functional properties: granulocytic MDSCs (G-MDSCs) and monocytic MDSCs (M-MDSCs). Recently greatest attention has been paid to innate immunity in Multiple Sclerosis (MS), so the aim of our review is to provide an overview of the main characteristics of MDSCs in MS and its preclinical model by discussing the most recent data available. The immunosuppressive functions of MDSCs can be dysregulated in MS, leading to an exacerbation of the autoimmune response and disease progression. Antigen-specific peptide immunotherapy, which aims to restore tolerance while avoiding the use of non-specific immunosuppressive drugs, is a promising approach for autoimmune diseases, but the cellular mechanisms behind successful therapy remain poorly understood. Therefore, targeting MDSCs could be a promising therapeutic approach for MS. Various strategies for modulating MDSCs have been investigated, including the use of pharmacological agents, biological agents, and adoptive transfer of exogenous MDSCs. However, it remained unclear whether MDSCs display any therapeutic potential in MS and how this therapy could modulate different aspects of the disease. Collectively, all the described studies revealed a pivotal role for MDSCs in the regulation of MS.

Mathematical Modeling for Oscillations Driven by Noncoding RNAs.

In this chapter, we first survey strategies for the mathematical modeling of gene regulatory networks for capturing physiologically important dynamics in cells such as oscillations. We focus on models based on ordinary differential equations with various forms of nonlinear functions that describe gene regulations. We next use a small system of a microRNA and its mRNA target to illustrate a recently discovered oscillator driven by noncoding RNAs. This oscillator has unique features that distinguish it from conventional biological oscillators, including the absence of an imposed negative feedback loop and the divergence of the periods. The latter property may serve crucial biological functions for restoring heterogeneity of cell populations on the timescale of days. We describe general requirements for obtaining the limit cycle oscillations in terms of underlying biochemical reactions and kinetic rate constants. We discuss future directions stemming from this minimal, noncoding RNA-based model for gene expression oscillation.

ImmuNet: a segmentation-free machine learning pipeline for immune landscape phenotyping in tumors by multiplex imaging.

Tissue specimens taken from primary tumors or metastases contain important information for diagnosis and treatment of cancer patients. Multiplex imaging allows in situ visualization of heterogeneous cell populations, such as immune cells, in tissue samples. Most image processing pipelines first segment cell boundaries and then measure marker expression to assign cell phenotypes. In dense tissue environments, this segmentation-first approach can be inaccurate due to segmentation errors or overlapping cells. Here, we introduce the machine-learning pipeline "ImmuNet", which identifies positions and phenotypes of cells without segmenting them. ImmuNet is easy to train: human annotators only need to click on an immune cell and score its expression of each marker-drawing a full cell outline is not required. We trained and evaluated ImmuNet on multiplex images from human tonsil, lung cancer, prostate cancer, melanoma, and bladder cancer tissue samples and found it to consistently achieve error rates below 5%-10% across tissue types, cell types, and tissue densities, outperforming a segmentation-based baseline method. Furthermore, we externally validate ImmuNet results by comparing them to flow cytometric cell count measurements from the same tissue. In summary, ImmuNet is an effective, simpler alternative to segmentation-based approaches when only cell positions and phenotypes, but not their shapes, are required for downstream analyses. Thus, ImmuNet helps researchers to analyze cell positions in multiplex tissue images more easily and accurately.

Insulin and leptin acutely modulate the energy metabolism of primary hypothalamic and cortical astrocytes.

Astrocytes constitute a heterogeneous cell population within the brain, contributing crucially to brain homeostasis and playing an important role in overall brain function. Their function and metabolism are not only regulated by local signals, for example, from nearby neurons, but also by long-range signals such as hormones. Thus, two prominent hormones primarily known for regulating the energy balance of the whole organism, insulin, and leptin, have been reported to also impact astrocytes within the brain. In this study, we investigated the acute regulation of astrocytic metabolism by these hormones in cultured astrocytes prepared from the mouse cortex and hypothalamus, a pivotal region in the context of nutritional regulation. Utilizing genetically encoded, fluorescent nanosensors, the cytosolic concentrations of glucose, lactate, and ATP, along with glycolytic rate and the NADH/NAD+ redox state were measured. Under basal conditions, differences between the two populations of astrocytes were observed for glucose and lactate concentrations as well as the glycolytic rate. Additionally, astrocytic metabolism responded to insulin and leptin in both brain regions, with some unique characteristics for each cell population. Finally, both hormones influenced how cells responded to elevated extracellular levels of potassium ions, a common indicator of neuronal activity. In summary, our study provides evidence that insulin and leptin acutely regulate astrocytic metabolism within minutes. Additionally, while astrocytes from the hypothalamus and cortex share similarities in their metabolism, they also exhibit distinct properties, further underscoring the growing recognition of astrocyte heterogeneity.

Using Callus as an Ex Vivo System for Chromatin Analysis.

Next-generation sequencing has revolutionized epigenetics research, enabling a comprehensive analysis of DNA methylation and histone modification profiles to explore complex biological systems at unprecedented depth. Deciphering the intricate epigenetic mechanisms that regulate gene activity presents significant challenges, including the issue of analyzing heterogeneous cell populations in bulk. Bulk analysis introduces bias and can obscure crucial information by averaging readouts from distinct cells. Various approaches have been developed to address this issue, such as cell-type-specific enrichment or single-cell sequencing techniques. However, the need for transgenic lines with fluorescent markers, along with technical challenges such as efficient protoplast isolation and low yield, limits their widespread adoption and use in multi-omic studies. This review discusses the pros and cons of these approaches, providing a valuable basis for selecting the most suitable strategy to minimize heterogeneity. We will also highlight the use of cotyledon-derived callus as an ex vivo system as a simple, accessible, and robust platform for enabling high-throughput multi-omic analyses.

Trajectory inference from single-cell genomics data with a process time model.

Single-cell transcriptomics experiments provide gene expression snapshots of heterogeneous cell populations across cell states. These snapshots have been used to infer trajectories and dynamic information even without intensive, time-series data by ordering cells according to gene expression similarity. However, while single-cell snapshots sometimes offer valuable insights into dynamic processes, current methods for ordering cells are limited by descriptive notions of "pseudotime" that lack intrinsic physical meaning. Instead of pseudotime, we propose inference of "process time" via a principled modeling approach to formulating trajectories and inferring latent variables corresponding to timing of cells subject to a biophysical process. Our implementation of this approach, called Chronocell, provides a biophysical formulation of trajectories built on cell state transitions. The Chronocell model is identifiable, making parameter inference meaningful. Furthermore, Chronocell can interpolate between trajectory inference, when cell states lie on a continuum, and clustering, when cells cluster into discrete states. By using a variety of datasets ranging from cluster-like to continuous, we show that Chronocell enables us to assess the suitability of datasets and reveals distinct cellular distributions along process time that are consistent with biological process times. We also compare our parameter estimates of degradation rates to those derived from metabolic labeling datasets, thereby showcasing the biophysical utility of Chronocell. Nevertheless, based on performance characterization on simulations, we find that process time inference can be challenging, highlighting the importance of dataset quality and careful model assessment.