The tumor microenvironment and triple-negative breast cancer aggressiveness: shedding light on mechanisms and targeting

INTRODUCTION

In contrast to other breast cancer subtypes, there are currently limited options of targeted therapies for triple-negative breast cancer (TNBC). Immense research has demonstrated that not only cancer cells but also stromal cells and immune cells in the tumor microenvironment (TME) play significant roles in the progression of TNBC. It is thus critical to understand the components of the TME of TNBC and the interactions between the various cell populations.

AREAS COVERED

The components of the TME of TNBC identified by single-cell technologies are reviewed. Furthermore, the molecular interactions between the cells and the potential therapeutic targets contributing to the progression of TNBC are discussed.

EXPERT OPINION

Single-cell omics studies have contributed to the classification of cells in the TME and the identification of important cell types involved in the progression and the treatment of the tumor. The interactions between cancer cells and stromal cells/immune cells in the TME have led to the discovery of potential therapeutic targets. Experimental data with spatial and temporal resolution will further boost the understanding of the TME of TNBC.

Persistent alveolar type 2 dysfunction and lung structural derangement in post-acute COVID-19

SARS-CoV-2 infection can manifest as a wide range of respiratory and systemic symptoms well after the acute phase of infection in over 50% of patients. Key questions remain on the long-term effects of infection on tissue pathology in recovered COVID-19 patients. To address these questions we performed multiplexed imaging of post-mortem lung tissue from 12 individuals who died post-acute COVID-19 (PC) and compare them to lung tissue from patients who died during the acute phase of COVID-19, or patients who died with idiopathic pulmonary fibrosis (IPF), and otherwise healthy lung tissue. We find evidence of viral presence in the lung up to 359 days after the acute phase of disease, including in patients with negative nasopharyngeal swab tests. The lung of PC patients are characterized by the accumulation of senescent alveolar type 2 cells, fibrosis with hypervascularization of peribronchial areas and alveolar septa, as the most pronounced pathophysiological features. At the cellular level, lung disease of PC patients, while distinct, shares pathological features with the chronic pulmonary disease of IPF. which may help rationalize interventions for PC patients. Altogether, this study provides an important foundation for the understanding of the long-term effects of SARS-CoV-2 pulmonary infection at the microanatomical, cellular, and molecular level.

Organic Molecular Probe Enabled Ionic Current Rectification toward Subcellular Detection of Glutathione with High Selectivity, Sensitivity, and Recyclability

Single-cell interrogation with the solid-state nanoprobes enables understanding of the linkage between cellular behavior and heterogeneity. Herein, inspired by the charge property of the organic molecular probe (OMP), a generic ionic current rectification (ICR) single-cell methodology is established, exemplified by subcellular detection of glutathione (GSH) with high selectivity, sensitivity, and recyclability. The as-developed nanosensor can transduce the subcellular OMP-GSH interaction via a sensitive ionic response, which stems from the superior specificity of OMP and its essential charge property. In addition, the nanosensor exhibits good reversibility, since the subsequent tandem reaction after the recognition can well recover the sensing surface. Given the diverse structures and tailorable charge properties of OMP, this work underpins a new and general method of OMP-based ICR single-cell analysis.

Tumor-intrinsic SIRPA promotes sensitivity to checkpoint inhibition immunotherapy in melanoma

Checkpoint inhibition immunotherapy has revolutionized cancer treatment, but many patients show resistance. Here we perform integrative transcriptomic and proteomic analyses on emerging immuno-oncology targets across multiple clinical cohorts of melanoma under anti-PD-1 treatment, on both bulk and single-cell levels. We reveal a surprising role of tumor-intrinsic SIRPA in enhancing antitumor immunity, in contrast to its well-established role as a major inhibitory immune modulator in macrophages. The loss of SIRPA expression is a marker of melanoma dedifferentiation, a key phenotype linked to immunotherapy efficacy. Inhibition of SIRPA in melanoma cells abrogates tumor killing by activated CD8+ T cells in a co-culture system. Mice bearing SIRPA-deficient melanoma tumors show no response to anti-PD-L1 treatment, whereas melanoma-specific SIRPA overexpression significantly enhances immunotherapy response. Mechanistically, SIRPA is regulated by its pseudogene, SIRPAP1. Our results suggest a complicated role of SIRPA in the tumor ecosystem, highlighting cell-type-dependent antagonistic effects of the same target on immunotherapy.

Single-cell mass spectrometry

Owing to recent advances in mass spectrometry (MS), tens to hundreds of proteins, lipids, and small molecules can be measured in single cells. The ability to characterize the molecular heterogeneity of individual cells is necessary to define the full assortment of cell subtypes and identify their function. We review single-cell MS including high-throughput, targeted, mass cytometry-based approaches and antibody-free methods for broad profiling of the proteome and metabolome of single cells. The advantages and disadvantages of different methods are discussed, as well as the challenges and opportunities for further improvements in single-cell MS. These methods is being used in biomedicine in several applications including revealing tumor heterogeneity and high-content drug screening.

Single-cell transcriptomic, transcriptomic, and metabolomic characterization of human atherosclerosis

Background

Atherosclerosis is the main cause of many cardiovascular and cerebrovascular diseases (CVDs), and gaining a deeper understanding of the intercellular connections and key central genes which mediate formation of atherosclerotic plaques is required.

Methods

We performed a comprehensive bioinformatics analysis of differential genetic screening, Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway annotation, protein-protein interactions (PPIs), pseudo-timing, intercellular communication, transcription factors on carotid single-cell sequencing data, and aortic bulk transcriptome and metabolomic data.

Results

Ten cell types were identified in the data: T cells, monocytes, smooth muscle cells, endothelial cells, B cells, fibroblasts, plasma cells, mast cells, dendritic cells, and natural killer cells. Endothelial, fibroblast, macrophage, and smooth muscle cell subtype differentiation trajectories, interaction networks, and important transcription factors were characterized in detail. Finally, using this information combined with transcriptome and metabolome analyses, we found the key genes and signaling pathways of atherosclerosis, especially the advanced glycation end products and receptor for advanced glycation end products signaling pathway (AGE-RAGE signaling pathway) in diabetic complications, linked the differential metabolites with fibroblasts and atherosclerosis and contributed to it in patients with diabetes.

Conclusions

Collectively, this study provides key genes, signaling pathways, cellular communication, and transcription factors among endothelial cells, fibroblasts, macrophages, and smooth muscle cells for the study of atherosclerotic plaques, and provides a basis for the diagnosis and treatment of atherosclerosis-like sclerosis.

Comparison of Different Label-Free Raman Spectroscopy Approaches for the Discrimination of Clinical MRSA and MSSA Isolates

Methicillin-resistant Staphylococcus aureus (MRSA) is classified as one of the priority pathogens that threaten human health. Resistance detection with conventional microbiological methods takes several days, forcing physicians to administer empirical antimicrobial treatment that is not always appropriate. A need exists for a rapid, accurate, and cost-effective method that allows targeted antimicrobial therapy in limited time. In this pilot study, we investigate the efficacy of three different label-free Raman spectroscopic approaches to differentiate methicillin-resistant and -susceptible clinical isolates of S. aureus (MSSA). Single-cell analysis using 532 nm excitation was shown to be the most suitable approach since it captures information on the overall biochemical composition of the bacteria, predicting 87.5% of the strains correctly. UV resonance Raman microspectroscopy provided a balanced accuracy of 62.5% and was not sensitive enough in discriminating MRSA from MSSA. Excitation of 785 nm directly on the petri dish provided a balanced accuracy of 87.5%. However, the difference between the strains was derived from the dominant staphyloxanthin bands in the MRSA, a cell component not associated with the presence of methicillin resistance. This is the first step toward the development of label-free Raman spectroscopy for the discrimination of MRSA and MSSA using single-cell analysis with 532 nm excitation. IMPORTANCE Label-free Raman spectra capture the high chemical complexity of bacterial cells. Many different Raman approaches have been developed using different excitation wavelength and cell analysis methods. This study highlights the major importance of selecting the most suitable Raman approach, capable of providing spectral features that can be associated with the cell mechanism under investigation. It is shown that the approach of choice for differentiating MRSA from MSSA should be single-cell analysis with 532 nm excitation since it captures the difference in the overall biochemical composition. These results should be taken into consideration in future studies aiming for the development of label-free Raman spectroscopy as a clinical analytical tool for antimicrobial resistance determination.

Multiplexed Digital Spatial Protein Profiling Reveals Distinct Phenotypes of Mononuclear Phagocytes in Livers with Advanced Fibrosis

Background and Aims: Intrahepatic mononuclear phagocytes (MPs) are critical for the initiation and progression of liver fibrosis. In this study, using multiplexed digital spatial protein profiling, we aimed to derive a unique protein signature predicting advanced liver fibrosis. Methods: Snap-frozen liver tissues from various chronic liver diseases were subjected to spatially defined protein-based multiplexed profiling (Nanostring GeoMX^TM). A single-cell RNA sequencing analysis was performed using Gene Expression Omnibus (GEO) datasets from normal and cirrhotic livers. Results: Sixty-four portal regions of interest (ROIs) were selected for the spatial profiling. Using the results from the CD68⁺ area, a highly sensitive and specific immune-related protein signature (CD68, HLA-DR, OX40L, phospho-c-RAF, STING, and TIM3) was developed to predict advanced (F3 and F4) fibrosis. A combined analysis of single-cell RNA sequencing data from GEO datasets (GSE136103) and spatially-defined, protein-based multiplexed profiling revealed that most proteins upregulated in F0-F2 livers in portal CD68⁺ cells were specifically marked in tissue monocytes, whereas proteins upregulated in F3 and F4 livers were marked in scar-associated macrophages (SAMacs) and tissue monocytes. Internal validation using mRNA expression data with the same cohort tissues demonstrated that mRNA levels for TREM2, CD9, and CD68 are significantly higher in livers with advanced fibrosis. Conclusions: In patients with advanced liver fibrosis, portal MPs comprise of heterogeneous populations composed of SAMacs, Kupffer cells, and tissue monocytes. This is the first study that used spatially defined protein-based multiplexed profiling, and we have demonstrated the critical difference in the phenotypes of portal MPs between livers with early- or late-stage fibrosis.

Characteristic analysis of skin keratinocytes in patients with type 2 diabetes based on the single-cell levels

BACKGROUND

Keratinocytes play an important role in wound healing; however, less is known about skin keratinocytes in patients with type 2 diabetes mellitus (T2DM). Therefore, this study aimed to search for the transcriptional characteristics of keratinocytes at the single-cell level from T2DM patients, and to provide experimental data for identifying the pathological mechanisms of keratinocytes under pathological conditions.

METHODS

We performed single-cell RNA sequencing on the skin tissue from two T2DM patients and one patient without diabetes-induced trauma using the BD Rhapsody™ Single-Cell Analysis System. With the help of bioinformatics R-based single-cell analysis software, we analyzed the results of single-cell sequencing to identify the single-cell subsets and transcriptional characteristics of keratinocytes at the single-cell level, including Kyoto Encyclopedia of Genes and Genomes and Gene Ontology analyzes.

RESULTS

In this study, we found specific highly expressed signature keratinocyte-related genes. We analyzed the transcriptome of keratinocytes from experimental and control groups and screened a total of 356 differential genes, which were subject to bioinformatics analysis. Enriched pathways included oxidative phosphorylation, antigen processing and presentation, prion and Huntingtons' diseases, bacterial invasion of epithelial cells, thermogenesis, vasopressin-regulated water reabsorption, and protein processing in the endoplasmic reticulum.

CONCLUSIONS

This study revealed the characteristics of keratinocytes at the single-cell level and screened a group of differentially expressed genes related to T2DM-associated keratinocytes, oxidative phosphorylation, cytokine receptor interactions, prion diseases, and other signaling pathways.

Loss of apelin blocks the emergence of sprouting angiogenesis in experimental tumors

Angiogenesis inhibitor drugs targeting vascular endothelial growth factor (VEGF) signaling to the endothelial cell (EC) are used to treat various cancer types. However, primary or secondary resistance to therapy is common. Clinical and pre-clinical studies suggest that alternative pro-angiogenic factors are upregulated after VEGF pathway inhibition. Therefore, identification of alternative pro-angiogenic pathway(s) is critical for the development of more effective anti-angiogenic therapy. Here we study the role of apelin as a pro-angiogenic G-protein-coupled receptor ligand in tumor growth and angiogenesis. We found that loss of apelin in mice delayed the primary tumor growth of Lewis lung carcinoma 1 and B16F10 melanoma when combined with the VEGF receptor tyrosine kinase inhibitor, sunitinib. Targeting apelin in combination with sunitinib markedly reduced the tumor vessel density, and decreased microvessel remodeling. Apelin loss reduced angiogenic sprouting and tip cell marker gene expression in comparison to the sunitinib-alone-treated mice. Single-cell RNA sequencing of tumor EC demonstrated that the loss of apelin prevented EC tip cell differentiation. Thus, apelin is a potent pro-angiogenic cue that supports initiation of tumor neovascularization. Together, our data suggest that targeting apelin may be useful as adjuvant therapy in combination with VEGF signaling inhibition to inhibit the growth of advanced tumors.

Probabilistic Genotyping of Single Cell Replicates from Mixtures Involving First-Degree Relatives Prevents the False Inclusions of Non-Donor Relatives

Analysis of complex DNA mixtures comprised of related individuals requires a great degree of care due to the increased risk of falsely including non-donor first-degree relatives. Although alternative likelihood ratio (LR) propositions that may aid in the analysis of these difficult cases can be employed, the prior information required for their use is not always known, nor do these alternative propositions always prevent false inclusions. For example, with a father/mother/child mixture, conditioning the mixture on the presence of one of the parents is recommended. However, the definitive presence of the parent(s) is not always known and an assumption of their presence in the mixture may not be objectively justifiable. Additionally, the high level of allele sharing seen with familial mixtures leads to an increased risk of underestimating the number of contributors (NOC) to a mixture. Therefore, fully resolving and identifying each of the individuals present in familial mixtures and excluding related non-donors is an important goal of the mixture deconvolution process and can be of great investigative value. Here, firstly, we further investigated and confirmed the problems encountered with standard bulk analysis of familial mixtures and demonstrated the ability of single cell analysis to fully distinguish first-degree relatives (FDR). Then, separation of each of the individual donors via single cell analysis was carried out by a combination of direct single cell subsampling (DSCS), enhanced DNA typing, and probabilistic genotyping, and applied to three complex familial 4-person mixtures resulting in a probative gain of LR for all donors and an accurate determination of the NOC. Significantly, non-donor first-degree relatives that were falsely included (LRs > 102−108) by a standard bulk sampling and analysis approach were no longer falsely included using DSCS.

Accurate tumor clonal structures require single-cell analysis

Tumor clonal structure is closely related to future progression, which has been mainly investigated as mutation abundance clustering in bulk samples. With relatively limited studies at single-cell resolution, a systematic comparison of the two approaches is still lacking. Here, using bulk and single-cell mutational data from the liver and colorectal cancers, we checked whether co-mutations determined by single-cell analysis had corresponding bulk variant allele frequency (VAF) peaks. While bulk analysis suggested the absence of subclonal peaks and, possibly, neutral evolution in some cases, the single-cell analysis identified coexisting subclones. The overlaps of bulk VAF ranges for co-mutations from different subclones made it difficult to separate them. Complex subclonal structures and dynamic evolution could be hidden under the seemingly clonal neutral pattern at the bulk level, suggesting single-cell analysis is necessary to avoid underestimation of tumor heterogeneity.

Specific Localization and Quantification of the Oligo-Mouse-Microbiota (OMM12 ) by Fluorescence In Situ Hybridization (FISH)

The oligo-mouse-microbiota (OMM¹² ) is a widely used syncom that colonizes gnotobiotic mice in a stable manner. It provides several fundamental functions to its murine host, including colonization resistance against enteric pathogens. Here, we designed and validated specific fluorescence in situ hybridization (FISH) probes to detect and quantify OMM¹² strains on intestinal tissue cross sections. 16S rRNA-specific probes were designed, and specificity was validated on fixed pure cultures. A hybridization protocol was optimized for sensitive detection of the individual bacterial cells in cryosections. Using this method, we showed that the intestinal mucosal niche of Akkermansia muciniphila can be influenced by global gut microbial community context. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol: Localization and quantification of OMM¹² single strains in mouse cecum cross section Support Protocol: Establishment of specific FISH probe set for OMM¹² syncom.

Single-Cell Analysis of the Antimicrobial and Bactericidal Activities of the Antimicrobial Peptide Magainin 2

Antimicrobial peptides (AMPs) inhibit the proliferation of or kill bacterial cells. To measure these activities, several methods have been used, which provide only the average value of many cells. Here, we report the development of a method to examine the antimicrobial and bactericidal activities of AMPs at the single-cell level (i.e., single-cell analysis) and apply this strategy to examine the interaction of an AMP, magainin 2 (Mag), with Escherichia coli cells. Using this method, we monitored the proliferation of single cells on agar in a microchamber and measured the distribution of the number of cells in each microcolony using optical microscopy. For method A, we incubated cells in the presence of various concentrations of AMPs for 3 h. The fraction of microcolonies containing only a single cell, Psingle, increased with the Mag concentration and reached 1 at a specific concentration, which corresponded to the MIC. For method B, after the interaction of a cell suspension with an AMP for a specific time, an aliquot was diluted to stop the interaction, and the proliferation of single cells then was monitored after a 3-h incubation; this method permits the definition of Psingle(t), the fraction of dead cells after the interaction. For the interaction of Mag with E. coli cells, Psingle(t) increased with the interaction time, reaching ~1 at 10 and 20 min for 25 and 13 μM Mag, respectively. Thus, these results indicate that a short interaction time between Mag and E. coli cells is sufficient to induce bacterial cell death. IMPORTANCE To elucidate the activity of antimicrobial peptides (AMPs) against bacterial cells, it is important to estimate the interaction time that is sufficient to induce cell death. We have developed a method to examine the antimicrobial and bactericidal activities of AMPs at the single-cell level (i.e., single-cell analysis). Using this method, we monitored the proliferation of single cells on agar in a microchamber and measured the distribution of the number of cells in each microcolony using optical microscopy. We found that during the interaction of magainin 2 (Mag) with E. coli cells, the fraction of dead cells, Psingle(t), increased with the interaction time, rapidly reaching 1 (e.g., 10 min for 25 μM Mag). This result indicates that Mag induces cell death after a short time of interaction.

Cell Size as a Primary Determinant in Targeted Nanoparticle Uptake

Nanoparticle (NP) internalization by cells is complex, highly heterogeneous, and fundamentally important for nanomedicine. We report powerful probabilistic statistics from single-cell data on quantitative NP uptake of PEG-coated transferrin receptor-targeted gold NPs for cancer-derived and fibroblast cells according to their cell size, receptor expression, and receptor density. The smaller cancer cells had a greater receptor density and more efficient uptake of targeted NPs. However, simply due to fibroblasts being larger with more receptors, they exhibited greater NP uptake. While highly heterogeneous, targeted NP uptake strongly correlated with receptor expression. When uptake was normalized to cell size, no correlation existed. Consequently, skewed population distributions in cell sizes explain the distribution in NP uptake. Furthermore, exposure to the transferrin receptor-targeted NPs alters the fibroblast size and receptor expression, suggesting that the receptor-targeted NPs may interfere with the metabolic flux and nutrient exchange, which could assist in explaining the altered regulation of cells exposed to nanoparticles.

Scalable Additive Construction of Arrayed Microstructures with Encoded Properties for Bioimaging

Microarrays are essential components of analytical instruments. The elements of microarrays may be imbued with additional functionalities and encodings using composite materials and structures, but traditional microfabrication methods present substantial barriers to fabrication, design, and scalability. In this work, a tool-free technique was reported to additively batch-construct micromolded, composite, and arrayed microstructures. The method required only a compatible carrier fluid to deposit a material onto a substrate with some topography. Permutations of this basic fabrication approach were leveraged to gain control over the volumes and positions of deposited materials within the microstructures. As a proof of concept, cell micro-carrier arrays were constructed to demonstrate a range of designs, compositions, functionalities, and applications for composite microstructures. This approach is envisioned to enable the fabrication of complex composite biological and synthetic microelements for biosensing, cellular analysis, and biochemical screening.

Ultraprecise Real-Time Monitoring of Single Cells in Tumors in Response to Metal Ion-Mediated RNA Delivery

With the deepening of cancer clinical research, miRNAs provide new ideas for molecular diagnosis and treatment of tumors. Improving the molecular delivery efficiency of miRNA is the key to the success of miRNA therapy. We have established self-assembly diagnosis and treatment technologies that can be used to achieve accurate targeting and "cargo" delivery at the cellular level. This technology builds a miRNA (let-7a) delivery system based on metal precursor [Au(III) and Fe(II)]-mediated tumor microenvironmental response to realize the self-assembly of Au&Fe-miRNA complexes for precise real-time imaging of tumor cells and targeted therapy. To accurately measure the changes in reactive oxygen species during complex formation in real time at the single-cell level, we employed small-size nanoscale devices as analytical tools. This study proposes an electrochemical sensor based on carbon fiber electrodes for ultraprecise and multiple monitoring of metal-ion-mediated miRNA delivery systems, precisely realizing targeted tracking of tumors and effective intervention inhibition.

Single-Cell FISH Analysis Reveals Distinct Shifts in PKM Isoform Populations during Drug Resistance Acquisition

The Warburg effect, i.e., the utilization of glycolysis under aerobic conditions, is recognized as a survival advantage of cancer cells. However, how the glycolytic activity is affected during drug resistance acquisition has not been explored at single-cell resolution. Because the relative ratio of the splicing isoform of pyruvate kinase M (PKM), PKM2/PKM1, can be used to estimate glycolytic activity, we utilized a single-molecule fluorescence in situ hybridization (SM-FISH) method to simultaneously quantify the mRNA levels of PKM1 and PKM2. Treatment of HCT116 cells with gefitinib (GE) resulted in two distinct populations of cells. However, as cells developed GE resistance, the GE-sensitive population with reduced PKM2 expression disappeared, and GE-resistant cells (Res) demonstrated enhanced PKM1 expression and a tightly regulated PKM2/PKM1 ratio. Our data suggest that maintaining an appropriate PKM2 level is important for cell survival upon GE treatment, whereas increased PKM1 expression becomes crucial in GE Res. This approach demonstrates the importance of single-cell-based analysis for our understanding of cancer cell metabolic responses to drugs, which could aid in the design of treatment strategies for drug-resistant cancers.

AP-1 transcription factor network explains diverse patterns of cellular plasticity in melanoma cells

Cellular plasticity associated with fluctuations in transcriptional programs allows individual cells in a tumor to adopt heterogeneous differentiation states and switch phenotype during their adaptive responses to therapies. Despite increasing knowledge of such transcriptional programs, the molecular basis of cellular plasticity remains poorly understood. Here, we combine multiplexed transcriptional and protein measurements at population and single-cell levels with multivariate statistical modeling to show that the state of AP-1 transcription factor network plays a unifying role in explaining diverse patterns of plasticity in melanoma. We find that a regulated balance among AP-1 factors cJUN, JUND, FRA2, FRA1, and cFOS determines the intrinsic diversity of differentiation states and adaptive responses to MAPK inhibitors in melanoma cells. Perturbing this balance through genetic depletion of specific AP-1 proteins, or by MAPK inhibitors, shifts cellular heterogeneity in a predictable fashion. Thus, AP-1 may serve as a critical node for manipulating cellular plasticity with potential therapeutic implications.

2D MOF Nanosensor-Integrated Digital Droplet Microfluidic Flow Cytometry for In Situ Detection of Multiple miRNAs in Single CTC Cells

Current circulating tumor cells (CTCs) detection strategies based on surface epithelial markers suffer from low specificity in distinguishing between CTCs and epithelial cells in hematopoietic cell population. Tumor-associated miRNAs within CTCs are emerging as new biomarkers due to their high correlation with tumor development and progress. However, in-situ simultaneous analysis of multiple miRNAs in single CTC cell is still challenging. To overcome this limitation, a digital droplet microfluidic flow cytometry based on biofunctionalized 2D metal-organic framework nanosensor (Nano-DMFC) is developed for in situ detection of dual miRNAs simultaneously in single living breast cancer cells. Here, 2D MOF-based fluorescent resonance energy transfer (FRET) nanosensors are established by conjugating dual-color fluorescence dye-labeled DNA probes on MOF nanosheet surface. In the Nano-DMFC, 2D MOF-based nanoprobes are precisely microinjected into each single-cell encapsulated droplets to achieve dual miRNA characterization in single cancer cell. This Nano-DMFC platform successfully detects dual miRNAs at single-cell resolution in 10 mixed positive MCF-7 cells out of 10 000 negative epithelial cells in serum biomimic samples. Moreover, this Nano-DMFC platform shows good reproductivity in the recovery experiment of spiked blood samples, which demonstrate the high potential for CTC-based cancer early diagnosis and prognosis.

Single-cell transcriptomic analysis reveals circadian rhythm disruption associated with poor prognosis and drug-resistance in lung adenocarcinoma

Circadian rhythm disruption (CRD) represents a major contributor to tumor proliferation. Nonetheless, the role of CRD in the clinical prediction of cancer outcomes has not been well studied. In this study, we developed a computational algorithm, which was implemented in an open-source R package CRDscore, to define the intratumoral status of circadian disruption in three representative single-cell RNA-seq data sets of lung adenocarcinoma. We found that the malignant cells with high CRDscore were characterized by activation of glycolysis and epithelial-mesenchymal transition pathways. Furthermore, cell communication analysis indicated that CRD played a pivotal role in T cell exhaustion, which may be responsible for the poor prognosis of the malignancy. We then validated the findings with public bulk transcriptome datasets involving 22 cancer types. Cox regression analysis revealed that the CRDscore was a valuable prognostic biomarker. A model containing 23 circadian-related genes performed well in predicting immunotherapeutic outcomes in 14 independent cohorts. Importantly, decreased CRDscore was detect by RNA sequencing on H1299 cells with melatonin treatment. Meanwhile, the cells downregulated the expression level of SNAIL and TWIST, which contributed to an invasive phenotype. In conclusion, this study provides a novel computational framework for characterizing CRD status using single-cell transcriptomic data and further confirmed the molecular mechanisms underlying metabolic reprogramming and T cell exhaustion under CRD. The better understanding of the mechanisms may provide new possibilities for incorporating "anticancer approaches based on circadian clocks" into the treatment protocols of precision medicine.

Multiplexed single-cell analysis of FNA allows accurate diagnosis of salivary gland tumors

Diagnosing salivary gland tumors (SGTs) through fine-needle aspiration (FNA) biopsies is challenging due to the overlapping cytomorphologic features between benign and malignant tumors. The authors developed an innovative, multiplexed cycling technology for the rapid analyses of single cells obtained from FNA that can facilitate the molecular analyses and diagnosis of SGTs. Antibodies against 29 protein markers associated with 7 SGT subtypes were validated and chemically modified via custom linker-bio-orthogonal probes (FAST). Single-cell homogenates and FNA samples were profiled by FAST cyclic imaging and computational analysis. A prediction model was generated using a training set of 151,926 cells from primary SGTs (N = 26) and validated on a separate cohort (N = 30). Companion biomarker testing, such as neurotrophic tyrosine receptor kinase (NTRK), was also assessed with the FAST technology. The FAST molecular diagnostic assay was able to distinguish between benign and malignant SGTs with an accuracy of 0.86 for single-cell homogenate samples and 0.88 for FNA samples. Profiling of multiple markers as compared to a single marker increased the diagnostic accuracy (0.82 as compared to 0.65-0.74, respectively), independent of the cell number sampled. NTRK expression was also assessed by the FAST assay, highlighting the potential therapeutic application of this technology. Application of the novel multiplexed single-cell technology facilitates rapid biomarker testing from FNA samples at low cost. The customizable and modular FAST-FNA approach has relevance to multiple pathologies and organ systems where cytologic samples are often scarce and/or indeterminate resulting in improved diagnostic workflows and timely therapeutic clinical decision-making.

A Single-Cell Atlas of Tumor-Infiltrating Immune Cells in Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies with limited treatment options. To guide the design of more effective immunotherapy strategies, mass cytometry was employed to characterize the cellular composition of the PDAC-infiltrating immune cells. The expression of 33 protein markers was examined at the single-cell level in more than two million immune cells from four types of clinical samples, including PDAC tumors, normal pancreatic tissues, chronic pancreatitis tissues, and peripheral blood. Based on the analyses, we identified 23 distinct T-cell phenotypes, with some cell clusters exhibiting aberrant frequencies in the tumors. Programmed cell death protein 1 (PD-1) was extensively expressed in CD4+ and CD8+ T cells and coexpressed with both stimulatory and inhibitory immune markers. In addition, we observed elevated levels of functional markers, such as CD137L and CD69, in PDAC-infiltrating immune cells. Moreover, the combination of PD-1 and CD8 was used to stratify PDAC tumors from The Cancer Genome Atlas database into three immune subtypes, with S1 (PD-1+CD8+) exhibiting the best prognosis. Further analysis suggested distinct molecular mechanisms for immune exclusion in different subtypes. Taken together, the single-cell protein expression data depicted a detailed cell atlas of the PDAC-infiltrating immune cells and revealed clinically relevant information regarding useful cell phenotypes and targets for immunotherapy development.

Enabling Clonal Analyses of Yeast in Outer Space by Encapsulation and Desiccation in Hollow Microparticles

Studying microbes at the single-cell level in space can accelerate human space exploration both via the development of novel biotechnologies and via the understanding of cellular responses to space stressors and countermeasures. High-throughput technologies for screening natural and engineered cell populations can reveal cellular heterogeneity and identify high-performance cells. Here, we present a method to desiccate and preserve microbes in nanoliter-scale compartments, termed PicoShells, which are microparticles with a hollow inner cavity. In PicoShells, single cells are confined in an inner aqueous core by a porous hydrogel shell, allowing the diffusion of nutrients, wastes, and assay reagents for uninhibited cell growth and flexible assay protocols. Desiccated PicoShells offer analysis capabilities for single-cell derived colonies with a simple, low resource workflow, requiring only the addition of water to rehydrate hundreds of thousands of PicoShells and the single microbes encapsulated inside. Our desiccation method results in the recovery of desiccated microparticle morphology and porosity after a multi-week storage period and rehydration, with particle diameter and porosity metrics changing by less than 18% and 7%, respectively, compared to fresh microparticles. We also recorded the high viability of Saccharomyces cerevisiae yeast desiccated and rehydrated inside PicoShells, with only a 14% decrease in viability compared to non-desiccated yeast over 8.5 weeks, although we observed an 85% decrease in initial growth potential over the same duration. We show a proof-of-concept for a growth rate-based analysis of single-cell derived colonies in rehydrated PicoShells, where we identified 11% of the population that grows at an accelerated rate. Desiccated PicoShells thus provide a robust method for cell preservation before and during launch, promising a simple single-cell analysis method for studying heterogeneity in microbial populations in space.

Building the Plant SynBio Toolbox through Combinatorial Analysis of DNA Regulatory Elements

While the installation of complex genetic circuits in microorganisms is relatively routine, the synthetic biology toolbox is severely limited in plants. Of particular concern is the absence of combinatorial analysis of regulatory elements, the long design-build-test cycles associated with transgenic plant analysis, and a lack of naming standardization for cloning parts. Here, we use previously described plant regulatory elements to design, build, and test 91 transgene cassettes for relative expression strength. Constructs were transiently transfected into Nicotiana benthamiana leaves and expression of a fluorescent reporter was measured from plant canopies, leaves, and protoplasts isolated from transfected plants. As anticipated, a dynamic level of expression was achieved from the library, ranging from near undetectable for the weakest cassette to a ∼200-fold increase for the strongest. Analysis of expression levels in plant canopies, individual leaves, and protoplasts were correlated, indicating that any of the methods could be used to evaluate regulatory elements in plants. Through this effort, a well-curated 37-member part library of plant regulatory elements was characterized, providing the necessary data to standardize construct design for precision metabolic engineering in plants.

Identification of Pathogenic Immune Cell Subsets Associated With Checkpoint Inhibitor-Induced Myocarditis

BACKGROUND

Immune checkpoint inhibitors (ICIs) are monoclonal antibodies used to activate the immune system against tumor cells. Despite therapeutic benefits, ICIs have the potential to cause immune-related adverse events such as myocarditis, a rare but serious side effect with up to 50% mortality in affected patients. Histologically, patients with ICI myocarditis have lymphocytic infiltrates in the heart, implicating T cell-mediated mechanisms. However, the precise pathological immune subsets and molecular changes in ICI myocarditis are unknown.

METHODS

To identify immune subset(s) associated with ICI myocarditis, we performed time-of-flight mass cytometry on peripheral blood mononuclear cells from 52 individuals: 29 patients with autoimmune adverse events (immune-related adverse events) on ICI, including 8 patients with ICI myocarditis, and 23 healthy control subjects. We also used multiomics single-cell technology to immunophenotype 30 patients/control subjects using single-cell RNA sequencing, single-cell T-cell receptor sequencing, and cellular indexing of transcriptomes and epitopes by sequencing with feature barcoding for surface marker expression confirmation. To correlate between the blood and the heart, we performed single-cell RNA sequencing/T-cell receptor sequencing/cellular indexing of transcriptomes and epitopes by sequencing on MRL/Pdcd1^-/- (Murphy Roths large/programmed death-1-deficient) mice with spontaneous myocarditis.

RESULTS

Using these complementary approaches, we found an expansion of cytotoxic CD8⁺ T effector cells re-expressing CD45RA (Temra CD8⁺ cells) in patients with ICI myocarditis compared with control subjects. T-cell receptor sequencing demonstrated that these CD8⁺ Temra cells were clonally expanded in patients with myocarditis compared with control subjects. Transcriptomic analysis of these Temra CD8⁺ clones confirmed a highly activated and cytotoxic phenotype. Longitudinal study demonstrated progression of these Temra CD8⁺ cells into an exhausted phenotype 2 months after treatment with glucocorticoids. Differential expression analysis demonstrated elevated expression levels of proinflammatory chemokines (CCL5/CCL4/CCL4L2) in the clonally expanded Temra CD8⁺ cells, and ligand receptor analysis demonstrated their interactions with innate immune cells, including monocytes/macrophages, dendritic cells, and neutrophils, as well as the absence of key anti-inflammatory signals. To complement the human study, we performed single-cell RNA sequencing/T-cell receptor sequencing/cellular indexing of transcriptomes and epitopes by sequencing in Pdcd1^-/- mice with spontaneous myocarditis and found analogous expansions of cytotoxic clonal effector CD8⁺ cells in both blood and hearts of such mice compared with controls.

CONCLUSIONS

Clonal cytotoxic Temra CD8⁺ cells are significantly increased in the blood of patients with ICI myocarditis, corresponding to an analogous increase in effector cytotoxic CD8⁺ cells in the blood/hearts of Pdcd1^-/- mice with myocarditis. These expanded effector CD8⁺ cells have unique transcriptional changes, including upregulation of chemokines CCL5/CCL4/CCL4L2, which may serve as attractive diagnostic/therapeutic targets for reducing life-threatening cardiac immune-related adverse events in ICI-treated patients with cancer.

A Novel Necroptosis-Related Prognostic Signature of Glioblastoma Based on Transcriptomics Analysis and Single Cell Sequencing Analysis

Background: Glioblastoma (GBM) is the most common and deadly brain tumor. The clinical significance of necroptosis (NCPS) genes in GBM is unclear. The goal of this study is to reveal the potential prognostic NCPS genes associated with GBM, elucidate their functions, and establish an effective prognostic model for GBM patients. Methods: Firstly, the NCPS genes in GBM were identified by single-cell analysis of the GSE182109 dataset in the GEO database and weighted co-expression network analysis (WGCNA) of The Cancer Genome Atlas (TCGA) data. Three machine learning algorithms (Lasso, SVM-RFE, Boruta) combined with COX regression were used to build prognostic models. The subsequent analysis included survival, immune microenvironments, and mutations. Finally, the clinical significance of NCPS in GBM was explored by constructing nomograms. Results: We constructed a GBM prognostic model composed of NCPS-related genes, including CTSD, AP1S1, YWHAG, and IER3, which were validated to have good performance. According to the above prognostic model, GBM patients in the TCGA and CGGA groups could be divided into two groups according to NCPS, with significant differences in survival analysis between the two groups and a markedly worse prognostic status in the high NCPS group (p < 0.001). In addition, the high NCPS group had higher levels of immune checkpoint-related gene expression, suggesting that they may be more likely to benefit from immunotherapy. Conclusions: Four genes (CTSD, AP1S1, YWHAG, and IER3) were screened through three machine learning algorithms to construct a prognostic model for GBM. These key and novel diagnostic markers may become new targets for diagnosing and treating patients with GBM.

Leukemia-initiating HSCs in chronic lymphocytic leukemia reveal clonal leukemogenesis and differential drug sensitivity

The existence of "leukemia-initiating cells" (LICs) in chronic lymphocytic leukemia (CLL) remains controversial due to the difficulty in isolating and identifying the tumor-initiating cells. Here, we demonstrate a microchannel electroporation (MEP) microarray that injects RNA-detecting probes into single live cells, allowing the imaging and characterization of heterogeneous LICs by intracellular RNA expression. Using limited-cell FACS sequencing (LC-FACSeq), we can detect and monitor rare live LICs during leukemogenesis and characterize their differential drug sensitivity. Disease-associated mutation accumulation in developing B lymphoid but not myeloid lineage in CLL patient hematopoietic stem cells (CLL-HSCs), and development of independent clonal CLL-like cells in murine patient-derived xenograft models, suggests the existence of CLL LICs. Furthermore, we identify differential protein ubiquitination and unfolding response signatures in GATA2^high CLL-HSCs that exhibit increased sensitivity to lenalidomide and resistance to fludarabine compared to GATA2^lowCLL-HSCs. These results highlight the existence of therapeutically targetable disease precursors in CLL.

Integrated Workflow for the Label-Free Isolation and Genomic Analysis of Single Circulating Tumor Cells in Pancreatic Cancer

As pancreatic cancer is the third deadliest cancer in the U.S., the ability to study genetic alterations is necessary to provide further insight into potentially targetable regions for cancer treatment. Circulating tumor cells (CTCs) represent an especially aggressive subset of cancer cells, capable of causing metastasis and progressing the disease. Here, we present the Labyrinth-DEPArray pipeline for the isolation and analysis of single CTCs. Established cell lines, patient-derived CTC cell lines and freshly isolated CTCs were recovered and sequenced to reveal single-cell copy number variations (CNVs). The resulting CNV profiles of established cell lines showed concordance with previously reported data and highlight several gains and losses of cancer-related genes such as FGFR3 and GNAS. The novel sequencing of patient-derived CTC cell lines showed gains in chromosome 8q, 10q and 17q across both CTC cell lines. The pipeline was used to process and isolate single cells from a metastatic pancreatic cancer patient revealing a gain of chromosome 1q and a loss of chromosome 5q. Overall, the Labyrinth-DEPArray pipeline offers a validated workflow combining the benefits of antigen-free CTC isolation with single cell genomic analysis.

Application of non-negative matrix factorization in oncology: one approach for establishing precision medicine

The increase in the expectations of artificial intelligence (AI) technology has led to machine learning technology being actively used in the medical field. Non-negative matrix factorization (NMF) is a machine learning technique used for image analysis, speech recognition, and language processing; recently, it is being applied to medical research. Precision medicine, wherein important information is extracted from large-scale medical data to provide optimal medical care for every individual, is considered important in medical policies globally, and the application of machine learning techniques to this end is being handled in several ways. NMF is also introduced differently because of the characteristics of its algorithms. In this review, the importance of NMF in the field of medicine, with a focus on the field of oncology, is described by explaining the mathematical science of NMF and the characteristics of the algorithm, providing examples of how NMF can be used to establish precision medicine, and presenting the challenges of NMF. Finally, the direction regarding the effective use of NMF in the field of oncology is also discussed.

Distinct Cell-specific Roles of NOX2 and MyD88 in Epileptogenesis

It is well established that temporal lobe epilepsy (TLE) is often related to oxidative stress and neuroinflammation. Both processes subserve alterations observed in epileptogenesis and ultimately involve distinct classes of cells, including astrocytes, microglia, and specific neural subtypes. For this reason, molecules associated with oxidative stress response and neuroinflammation have been proposed as potential targets for therapeutic strategies. However, these molecules can participate in distinct intracellular pathways depending on the cell type. To illustrate this, we reviewed the potential role of nicotinamide adenine dinucleotide phosphate (NADPH) oxidase 2 (NOX2) and myeloid differentiation primary response 88 (MyD88) in astrocytes, microglia, and neurons in epileptogenesis. Furthermore, we presented approaches to study genes in different cells, employing single-cell RNA-sequencing (scRNAseq) transcriptomic analyses, transgenic technologies and viral serotypes carrying vectors with specific promoters. We discussed the importance of identifying particular roles of molecules depending on the cell type, endowing more effective therapeutic strategies to treat TLE.

Impedance-Enabled Camera-Free Intrinsic Mechanical Cytometry

Mechanical properties of single cells are important label-free biomarkers normally measured by expensive and complex imaging systems. To unlock this limit and allow mechanical properties comparable across different measurement platforms, camera-free intrinsic mechanical cytometry (CFIMC) is proposed for on-the-fly measurement of two major intrinsic mechanical parameters, that is, Young's modulus E and fluidity β, of single cells. CFIMC adopts a framework that couples the impedance electrodes with the constriction channel spatially, so that the impedance signals contain the dynamic deformability information of the cell squeezing through the constriction channel. Deformation of the cell is thus extracted from the impedance signals and used to derive the intrinsic mechanical parameters. With reasonably high throughput (>500 cells min^-1 ), CFIMC can successfully reveal the mechanical difference in cancer and normal cells (i.e., human breast cell lines MCF-10A, MCF-7, and MDA-MB-231), living and fixed cells, and pharmacological perturbations of the cytoskeleton. It is further found that 1 µM level concentration of Cytochalasin B may be the threshold for the treated cells to induce a significant cytoskeleton effect reflected by the mechanical parameters. It is envisioned that CFIMC provides an alternative avenue for high-throughput and real-time single-cell intrinsic mechanical analysis.

Kidney Development: Recent Insights from Technological Advances

The kidney is a complex organ, and how it forms is a fascinating process. New technologies, such as single-cell transcriptomics, and enhanced imaging modalities are offering new approaches to understand the complex and intertwined processes during embryonic kidney development.

Single-cell analyses highlight the proinflammatory contribution of C1q-high monocytes to Behçet's disease

Behçet's disease (BD) is a chronic vasculitis characterized by systemic immune aberrations. However, a comprehensive understanding of immune disturbances in BD and how they contribute to BD pathogenesis is lacking. Here, we performed single-cell and bulk RNA sequencing to profile peripheral blood mononuclear cells (PBMCs) and isolated monocytes from BD patients and healthy donors. We observed prominent expansion and transcriptional changes in monocytes in PBMCs from BD patients. Deciphering the monocyte heterogeneity revealed the accumulation of C1q-high (C1q^hi) monocytes in BD. Pseudotime inference indicated that BD monocytes markedly shifted their differentiation toward inflammation-accompanied and C1q^hi monocyte-ended trajectory. Further experiments showed that C1q^hi monocytes enhanced phagocytosis and proinflammatory cytokine secretion, and multiplatform analyses revealed the significant clinical relevance of this subtype. Mechanistically, C1q^hi monocytes were induced by activated interferon-γ (IFN-γ) signaling in BD patients and were decreased by tofacitinib treatment. Our study illustrates the BD immune landscape and the unrecognized contribution of C1q^hi monocytes to BD hyperinflammation, showing their potential as therapeutic targets and clinical assessment indexes.

Trapping and releasing of single microparticles and cells in a microfluidic chip

A microfluidic device was designed and fabricated to capture single microparticles and cells by using hydrodynamic force and selectively release the microparticles and cells of interest via negative dielectrophoresis by activating selected individual microelectrodes. The trap microstructure was optimized based on numerical simulation of the electric field as well as the flow field. The capture and selective release functions of the device were verified by multi-types microparticles with different diameters and K562 cells. The capture efficiencies/release efficiencies were 95.55% ± 0.43%/96.41% ± 1.08% and 91.34% ± 0.01%/93.67% ± 0.36% for microparticles and cells, respectively. By including more traps and microelectrodes, the device can achieve high throughput and realize the visual separation of microparticles/cells of interest in a large number of particle/cell groups.

Fast bacterial growth reduces antibiotic accumulation and efficacy

Phenotypic variations between individual microbial cells play a key role in the resistance of microbial pathogens to pharmacotherapies. Nevertheless, little is known about cell individuality in antibiotic accumulation. Here, we hypothesise that phenotypic diversification can be driven by fundamental cell-to-cell differences in drug transport rates. To test this hypothesis, we employed microfluidics-based single-cell microscopy, libraries of fluorescent antibiotic probes and mathematical modelling. This approach allowed us to rapidly identify phenotypic variants that avoid antibiotic accumulation within populations of Escherichia coli, Pseudomonas aeruginosa, Burkholderia cenocepacia, and Staphylococcus aureus. Crucially, we found that fast growing phenotypic variants avoid macrolide accumulation and survive treatment without genetic mutations. These findings are in contrast with the current consensus that cellular dormancy and slow metabolism underlie bacterial survival to antibiotics. Our results also show that fast growing variants display significantly higher expression of ribosomal promoters before drug treatment compared to slow growing variants. Drug-free active ribosomes facilitate essential cellular processes in these fast-growing variants, including efflux that can reduce macrolide accumulation. We used this new knowledge to eradicate variants that displayed low antibiotic accumulation through the chemical manipulation of their outer membrane inspiring new avenues to overcome current antibiotic treatment failures.

Expression Patterns of Energy-Related Genes in Single Cells Uncover Key Isoforms and Enzymes That Gain Priority Under Nanoparticle-Induced Stress

Cellular responses to nanoparticles (NPs) have been largely studied in cell populations, providing averaged values that often misrepresent the true molecular processes that occur in the individual cell. To understand how a cell redistributes limited molecular resources to achieve optimal response and survival requires single-cell analysis. Here we applied multiplex single molecule-based fluorescence in situ hybridization (fliFISH) to quantify the expression of 10 genes simultaneously in individual intact cells, including glycolysis and glucose transporter genes, which are critical for restoring and maintaining energy balance. We focused on individual gill epithelial cell responses to lithium cobalt oxide (LCO) NPs, which are actively pursued as cathode materials in lithium-ion batteries, raising concerns about their impact on the environment and human health. We found large variabilities in the expression levels of all genes between neighboring cells under the same exposure conditions, from only a few transcripts to over 100 copies in individual cells. Gene expression ratios among the 10 genes in each cell uncovered shifts in favor of genes that play key roles in restoring and maintaining energy balance. Among these genes are isoforms that can secure and increase glycolysis rates more efficiently, as well as genes with multiple cellular functions, in addition to glycolysis, including DNA repair, regulation of gene expression, cell cycle progression, and proliferation. Our study uncovered prioritization of gene expression in individual cells for restoring energy balance under LCO NP exposures. Broadly, our study gained insight into single-cell strategies for redistributing limited resources to achieve optimal response and survival under stress.

Suspendable Hydrogel Nanovials for Massively Parallel Single-Cell Functional Analysis and Sorting

Techniques to analyze and sort single cells based on functional outputs, such as secreted products, have the potential to transform our understanding of cellular biology as well as accelerate the development of next-generation cell and antibody therapies. However, secreted molecules rapidly diffuse away from cells, and analysis of these products requires specialized equipment and expertise to compartmentalize individual cells and capture their secretions. Herein, we describe methods to fabricate hydrogel-based chemically functionalized microcontainers, which we call nanovials, and demonstrate their use for sorting single viable cells based on their secreted products at high-throughput using only commonly accessible laboratory infrastructure. These nanovials act as solid supports that facilitate attachment of a variety of adherent and suspension cell types, partition uniform aqueous compartments, and capture secreted proteins. Solutions can be exchanged around nanovials to perform fluorescence immunoassays on secreted proteins. Using this platform and commercial flow sorters, we demonstrate high-throughput screening of stably and transiently transfected producer cells based on relative IgG production. Chinese hamster ovary cells sorted based on IgG production regrew and maintained a high secretion phenotype over at least a week, yielding >40% increase in bulk IgG production rates. We also sorted hybridomas and B lymphocytes based on antigen-specific antibody production. Hybridoma cells secreting an antihen egg lysozyme antibody were recovered from background cells, enriching a population of ∼4% prevalence to >90% following sorting. Leveraging the high-speed sorting capabilities of standard sorters, we sorted >1 million events in <1 h. IgG secreting mouse B cells were also sorted and enriched based on antigen-specific binding. Successful sorting of antibody-secreting B cells combined with the ability to perform single-cell RT-PCR to recover sequence information suggests the potential to perform antibody discovery workflows. The reported nanovials can be easily stored and distributed among researchers, democratizing access to high-throughput functional cell screening.

Dissecting Intra-Tumoral Changes Following Immune Checkpoint Blockades in Intrahepatic Cholangiocarcinoma via Single-Cell Analysis

Purpose

To dissect the tumor ecosystem following immune checkpoint blockades (ICBs) in intrahepatic cholangiocarcinoma (ICC) at a single-cell level.

Methods

Single-cell RNA sequencing (scRNA-seq) data of 10 ICC patients for the ICB clinical trial were extracted from GSE125449 and systematically reanalyzed. Bulk RNA-seq data of 255 ICC patients were analyzed. Infiltration levels of SPP1⁺CD68⁺ tumor-associated macrophages (TAMs) were examined by dual immunofluorescence (IF) staining in 264 resected ICC samples. The correlation between SPP1⁺ TAMs and clinicopathological features as well as their prognostic significance was evaluated.

Results

Among the 10 patients, five received biopsy at baseline, and others were biopsied at different timings following ICBs. Single-cell transcriptomes for 5,931 cells were obtained. A tighter cellular communication network was observed in ICB-treated ICC. We found a newly emerging VEGF signaling mediated by PGF-VEGFR1 between cancer-associated fibroblasts (CAFs) and endothelial cells in ICC following ICBs. SPP1 expression was dramatically upregulated, and SPP1⁺ TAM gene signatures were enriched in TAMs receiving ICB therapy. We also identified SPP1⁺ TAMs as an independent adverse prognostic indicator for survival in ICC.

Conclusion

Our analyses provide an overview of the altered tumor ecosystem in ICC treated with ICBs and highlight the potential role of targeting CAFs and SPP1⁺TAMs in developing a more rational checkpoint blockade-based therapy for ICC.

Roles of the m6A Modification of RNA in the Glioblastoma Microenvironment as Revealed by Single-Cell Analyses

Purpose

Glioblastoma multiforme (GBM) is a common and aggressive form of brain tumor. The N⁶-methyladenosine (m⁶A) mRNA modification plays multiple roles in many biological processes and disease states. However, the relationship between m⁶A modifications and the tumor microenvironment in GBM remains unclear, especially at the single-cell level.

Experimental Design

Single-cell and bulk RNA-sequencing data were acquired from the GEO and TCGA databases, respectively. We used bioinformatics and statistical tools to analyze associations between m⁶A regulators and multiple factors.

Results

HNRNPA2B1 and HNRNPC were extensively expressed in the GBM microenvironment. m⁶A regulators promoted the stemness state in GBM cancer cells. Immune-related BP terms were enriched in modules of m⁶A-related genes. Cell communication analysis identified genes in the GALECTIN signaling network in GBM samples, and expression of these genes (LGALS9, CD44, CD45, and HAVCR2) correlated with that of m⁶A regulators. Validation experiments revealed that MDK in MK signaling network promoted migration and immunosuppressive polarization of macrophage. Expression of m⁶A regulators correlated with ICPs in GBM cancer cells, M2 macrophages and T/NK cells. Bulk RNA-seq analysis identified two expression patterns (low m⁶A/high ICP and high m⁶A/low ICP) with different predicted immune infiltration and responses to ICP inhibitors. A predictive nomogram model to distinguish these 2 clusters was constructed and validated with excellent performance.

Conclusion

At the single-cell level, m⁶A modification facilitates the stemness state in GBM cancer cells and promotes an immunosuppressive microenvironment through ICPs and the GALECTIN signaling pathway network. And we also identified two m⁶A-ICP expression patterns. These findings could lead to novel treatment strategies for GBM patients.

Integrative analysis of scRNA-seq and scATAC-seq revealed transit-amplifying thymic epithelial cells expressing autoimmune regulator

Medullary thymic epithelial cells (mTECs) are critical for self-tolerance induction in T cells via promiscuous expression of tissue-specific antigens (TSAs), which are controlled by the transcriptional regulator, AIRE. Whereas AIRE-expressing (Aire⁺) mTECs undergo constant turnover in the adult thymus, mechanisms underlying differentiation of postnatal mTECs remain to be discovered. Integrative analysis of single-cell assays for transposase-accessible chromatin (scATAC-seq) and single-cell RNA sequencing (scRNA-seq) suggested the presence of proliferating mTECs with a specific chromatin structure, which express high levels of Aire and co-stimulatory molecules, CD80 (Aire⁺CD80^hi). Proliferating Aire⁺CD80^hi mTECs detected using Fucci technology express a minimal number of Aire-dependent TSAs and are converted into quiescent Aire⁺CD80^hi mTECs expressing high levels of TSAs after a transit amplification. These data provide evidence for the existence of transit-amplifying Aire⁺mTEC precursors during the Aire⁺mTEC differentiation process of the postnatal thymus.

Density-Dependent Migration Characteristics of Cancer Cells Driven by Pseudopod Interaction

The ability of cancer cells to invade neighboring tissue from primary tumors is an important determinant of metastatic behavior. Quantification of cell migration characteristics such as migration speed and persistence helps to understand the requirements for such invasiveness. One factor that may influence invasion is how local tumor cell density shapes cell migration characteristics, which we here investigate with a combined experimental and computational modeling approach. First, we generated and analyzed time-lapse imaging data on two aggressive Triple-Negative Breast Cancer (TNBC) cell lines, HCC38 and Hs578T, during 2D migration assays at various cell densities. HCC38 cells exhibited a counter-intuitive increase in speed and persistence with increasing density, whereas Hs578T did not exhibit such an increase. Moreover, HCC38 cells exhibited strong cluster formation with active pseudopod-driven migration, especially at low densities, whereas Hs578T cells maintained a dispersed positioning. In order to obtain a mechanistic understanding of the density-dependent cell migration characteristics and cluster formation, we developed realistic spatial simulations using a Cellular Potts Model (CPM) with an explicit description of pseudopod dynamics. Model analysis demonstrated that pseudopods exerting a pulling force on the cell and interacting via increased adhesion at pseudopod tips could explain the experimentally observed increase in speed and persistence with increasing density in HCC38 cells. Thus, the density-dependent migratory behavior could be an emergent property of single-cell characteristics without the need for additional mechanisms. This implies that pseudopod dynamics and interaction may play a role in the aggressive nature of cancers through mediating dispersal.

History-dependent physiological adaptation to lethal genetic modification under antibiotic exposure

Genetic modifications, such as gene deletion and mutations, could lead to significant changes in physiological states or even cell death. Bacterial cells can adapt to diverse external stresses, such as antibiotic exposure, but can they also adapt to detrimental genetic modification? To address this issue, we visualized the response of individual Escherichia coli cells to deletion of the antibiotic resistance gene under chloramphenicol (Cp) exposure, combining the light-inducible genetic recombination and microfluidic long-term single-cell tracking. We found that a significant fraction (∼40%) of resistance-gene-deleted cells demonstrated a gradual restoration of growth and stably proliferated under continuous Cp exposure without the resistance gene. Such physiological adaptation to genetic modification was not observed when the deletion was introduced in 10 hr or more advance before Cp exposure. Resistance gene deletion under Cp exposure disrupted the stoichiometric balance of ribosomal large and small subunit proteins (RplS and RpsB). However, the balance was gradually recovered in the cell lineages with restored growth. These results demonstrate that bacterial cells can adapt even to lethal genetic modifications by plastically gaining physiological resistance. However, the access to the resistance states is limited by the environmental histories and the timings of genetic modification.

A phenotypic signature that identifies neoantigen-reactive T cells in fresh human lung cancers

A common theme across multiple successful immunotherapies for cancer is the recognition of tumor-specific mutations (neoantigens) by T cells. The rapid discovery of such antigen responses could lead to improved therapies through the adoptive transfer of T cells engineered to express neoantigen-reactive T cell receptors (TCRs). Here, through CITE-seq (cellular indexing of transcriptomes and epitopes by sequencing) and TCR-seq of non-small cell lung cancer (NSCLC) tumor-infiltrating lymphocytes (TILs), we develop a neoantigen-reactive T cell signature based on clonotype frequency and CD39 protein and CXCL13 mRNA expression. Screening of TCRs selected by the signature allows us to identify neoantigen-reactive TCRs with a success rate of 45% for CD8+ and 66% for CD4+ T cells. Because of the small number of samples analyzed (4 patients), generalizability remains to be tested. However, this approach can enable the quick identification of neoantigen-reactive TCRs and expedite the engineering of personalized neoantigen-reactive T cells for therapy.

Improving Cancer Immunotherapy: Exploring and Targeting Metabolism in Hypoxia Microenvironment

Although immunotherapy has achieved good results in various cancer types, a large proportion of patients are limited from the benefits. Hypoxia and metabolic reprogramming are the common and critical factors that impact immunotherapy response. Here, we present current research on the metabolism reprogramming induced by hypoxia on antitumor immunity and discuss the recent progression among preclinical and clinical trials exploring the therapeutic effects combining targeting hypoxia and metabolism with immunotherapy. By evaluating the little clinical translation of the combined therapy, we provide insight into “understanding and regulating cellular metabolic plasticity under the current tumor microenvironment (TME),” which is essential to explore the strategy for boosting immune responses by targeting the metabolism of tumor cells leading to harsh TMEs. Therefore, we highlight the potential value of advanced single-cell technology in revealing the metabolic heterogeneity and corresponding phenotype of each cell subtype in the current hypoxic lesion from the clinical patients, which can uncover potential metabolic targets and therapeutic windows to enhance immunotherapy.

Spatiotemporally Resolved Protein Detection in Live Cells Using Nanopore Biosensors

Spatiotemporal detection of proteins in living cells is a persistent challenge but is the key to understanding their cellular biology and developing theranostic technologies. We develop a dual-nanopore biosensor using affinity-tunable peptide probes, which enables label-free and spatiotemporal monitoring of protein abundance and its concentration change in single live cells. We demonstrate that by screening for peptide probes with tunable affinities, the nanopore modified with a medium-affinity peptide allowed reversible and sensitive detection of the protein kinase A (PKA) catalytic subunit with a detection limit of 0.04 nM. The sensor is shown to have the ability to effectively eliminate interferences from cell membrane resistance and coexisting species in live cell detection. Moreover, our sensor is successfully implemented in monitoring of dynamic PKA activity changes (PKA catalytic subunit dynamic concentration changes) under different stimulations in single live cells. Our design may provide a paradigm for developing nanopore biosensors for spatiotemporally resolved protein analysis in live cells.

Heterogeneity in extracellular vesicle secretion by single human macrophages revealed by super-resolution microscopy

The diverse origins, nanometre‐scale and invasive isolation procedures associated with extracellular vesicles (EVs) mean they are usually studied in bulk and disconnected from their parental cell. Here, we used super‐resolution microscopy to directly compare EVs secreted by individual human monocyte‐derived macrophages (MDMs). MDMs were differentiated to be M0‐, M1‐ or M2‐like, with all three secreting EVs at similar densities following activation. However, M0‐like cells secreted larger EVs than M1‐ and M2‐like macrophages. Proteomic analysis revealed variations in the contents of differently sized EVs as well as between EVs secreted by different MDM phenotypes. Super resolution microscopy of single‐cell secretions identified that the class II MHC protein, HLA‐DR, was expressed on ∼40% of EVs secreted from M1‐like MDMs, which was double the frequency observed for M0‐like and M2‐like EVs. Strikingly, human macrophages, isolated from the resected lungs of cancer patients, secreted EVs that expressed HLA‐DR at double the frequency and with greater intensity than M1‐like EVs. Quantitative analysis of single‐cell EV profiles from all four macrophage phenotypes revealed distinct secretion types, five of which were consistent across multiple sample cohorts. A sub‐population of M1‐like MDMs secreted EVs similar to lung macrophages, suggesting an expansion or recruitment of cells with a specific EV secretion profile within the lungs of cancer patients. Thus, quantitative analysis of EV heterogeneity can be used for single cell profiling and to reveal novel macrophage biology.

A disease-driver population within interstitial cells of human calcific aortic valves identified via single-cell and proteomic profiling

Cellular heterogeneity of aortic valves complicates the mechanistic evaluation of the calcification processes in calcific aortic valve disease (CAVD), and animal disease models are lacking. In this study, we identify a disease-driver population (DDP) within valvular interstitial cells (VICs). Through stepwise single-cell analysis, phenotype-guided omic profiling, and network-based analysis, we characterize the DDP fingerprint as CD44highCD29+CD59+CD73+CD45low and discover potential key regulators of human CAVD. These DDP-VICs demonstrate multi-lineage differentiation and osteogenic properties. Temporal proteomic profiling of DDP-VICs identifies potential targets for therapy, including MAOA and CTHRC1. In vitro loss-of-function experiments confirm our targets. Such a stepwise strategy may be advantageous for therapeutic target discovery in other disease contexts.

Capybara: A computational tool to measure cell identity and fate transitions

Measuring cell identity in development, disease, and reprogramming is challenging as cell types and states are in continual transition. Here, we present Capybara, a computational tool to classify discrete cell identity and intermediate "hybrid" cell states, supporting a metric to quantify cell fate transition dynamics. We validate hybrid cells using experimental lineage tracing data to demonstrate the multi-lineage potential of these intermediate cell states. We apply Capybara to diagnose shortcomings in several cell engineering protocols, identifying hybrid states in cardiac reprogramming and off-target identities in motor neuron programming, which we alleviate by adding exogenous signaling factors. Further, we establish a putative in vivo correlate for induced endoderm progenitors. Together, these results showcase the utility of Capybara to dissect cell identity and fate transitions, prioritizing interventions to enhance the efficiency and fidelity of stem cell engineering.

Single cell variability of CRISPR-Cas interference and adaptation

While CRISPR-Cas defence mechanisms have been studied on a population level, their temporal dynamics and variability in individual cells have remained unknown. Using a microfluidic device, time-lapse microscopy and mathematical modelling, we studied invader clearance in Escherichia coli across multiple generations. We observed that CRISPR interference is fast with a narrow distribution of clearance times. In contrast, for invaders with escaping PAM mutations we found large cell-to-cell variability, which originates from primed CRISPR adaptation. Faster growth and cell division and higher levels of Cascade increase the chance of clearance by interference, while slower growth is associated with increased chances of clearance by priming. Our findings suggest that Cascade binding to the mutated invader DNA, rather than spacer integration, is the main source of priming heterogeneity. The highly stochastic nature of primed CRISPR adaptation implies that only subpopulations of bacteria are able to respond quickly to invading threats. We conjecture that CRISPR-Cas dynamics and heterogeneity at the cellular level are crucial to understanding the strategy of bacteria in their competition with other species and phages.