Individual Cells Research Articles

Miniaturized protein profiling permits targeted signaling pathway analysis in individual circulating tumor cells to improve personalized treatment

BackgroundTraditional genomic profiling and mutation analysis of single cells like Circulating Tumor Cells (CTCs) fails to capture post-translational and functional alterations of proteins, often leading to limited treatment efficacy. To overcome this gap, we developed a miniaturized ‘protein analysis on the single cell level’ workflow—baptized ZeptoCTC. It integrates established technologies for single-cell isolation with sensitive Reverse Phase Protein Array (RPPA) analysis, thus enabling the comprehensive assessment of multiple protein expression and activation in individual CTCs.MethodsThe ZeptoCTC workflow involves several critical steps. Firstly, individual cells are labeled and isolated. This is followed by cell lysis and the printing of true single cell lysate preparations onto a ZeptoChip using a modified micromanipulator, CellCelector™. The printed lysates then undergo fluorescence immunoassay RPPA protein detection using a ZeptoReader. Finally, signal quantification is carried out with Image J software, ensuring precise measurement of multiple protein levels.ResultsThe efficacy of ZeptoCTC was demonstrated through various applications. Initially, it was used for measuring EpCAM protein expression, a standard marker for CTC detection, revealing higher levels in single MCF-7 over MDA-MB-231 tumor cells. Furthermore, in Capivasertib (Akt-inhibitor)-treated MCF-7 single cells, ZeptoCTC detected a 2-fold increase in the pAkt/Akt ratio compared to control cells, and confirmed co-performed bulk-cell western blot analysis results. Notably, when applied to individual CTCs from metastasized breast cancer patients, ZeptoCTC revealed significant differences in protein activation levels, particularly in measured pAkt and pErk levels, compared to patient-matched WBCs. Moreover, it successfully differentiated between CTCs from patients with different Akt1 genotypes, highlighting its potential to determine the activation status of druggable cancer driving proteins for individual and targeted treatment decision making.ConclusionsThe ZeptoCTC workflow represents a valuable tool in single cell cancer research, crucial for personalized medicine. It permits detailed analysis of key proteins and their activation status of targeted, cancer-driven signaling pathways in single cell samples, aiding in understanding tumor response, progression, and treatment efficacy beyond bulk analysis. The method significantly advances clinical investigations in cancer, improving treatment precision and effectiveness. The workflow will be applicable to protein analysis on other types of single cells like relevant in stem cell, neuropathology and hemopoietic cell research.

Robotic Manipulation of Cumulus–Oocyte Complexes for Cumulus Cell Removal

The removal of cumulus cells from cumulus–oocyte complexes is a critical step in clinical in vitro fertilization. Since the oocyte is partially occluded by the surrounding cumulus cells and individual cumulus cells are small in size, it is difficult for embryologists to assess the oocyte's maturity before cumulus cell removal and to completely remove all the cumulus cells manually . Furthermore, it is easy for the oocyte to become lost inside the micropipette during aspiration due to the inaccuracy of manual control. To deal with these difficulties, a robotic system was developed to completely remove cumulus cells from mature oocytes. In this study, an EPSANet50 network was developed to accurately assess the maturity of oocytes, avoiding the removal of cumulus cells around the immature oocyte. An adaptive controller was designed to accurately position oocytes at the target position, reducing the loss of oocytes inside the micropipette. An improved Yolov5s network was proposed to quantify the number and size of cumulus cells and assess the completeness of cumulus cell removal. The experimental results on mouse cumulus–oocyte complexes showed that the robotic system had a higher success rate (98.0 ± 1.8% vs. 85.3 ± 2.4%) and lower discard rate (4.1 ± 2.7% vs. 19.6 ± 3.5%) than the manual operation. Moreover, a higher amplification rate and lower non-specific rate were also achieved by the robotic system in the subsequent genetic testing procedure, indicating reduced genetic contamination from the cumulus cells.

Ex Vivo Analysis of the Association of GFP-Expressing L. aethiopica and L. mexicana with Human Peripheral Blood-Derived (PBD) Leukocytes over 24 Hours

Leishmania parasites are transmitted to mammalian hosts through the bite of sandflies. These parasites can infect phagocytic cells (macrophages, dendritic cells, and neutrophils) and non-phagocytic cells (B cells and fibroblasts). In mice models, the disease development or resolution is linked to T cell responses involving inflammatory cytokines and the activation of macrophages with the M1/M2 phenotype. However, this mechanism does not apply to human infection where a more complex immunological response occurs. The understanding of interactions between immune cells during Leishmania infection in humans is still limited, as current infection models focus on individual cell types or late infection using controlled human infection models (CHIMs). This study investigated the early parasite infection in freshly isolated peripheral blood-derived (PBD) leukocytes over 24 h. Flow cytometer analysis is used in immunophenotyping to identify different subpopulations. The study found that among the L. aethiopicaGFP-associated leukocytes, most cells were neutrophils (55.87% ± 0.09 at 4 h) and monocytes (23.50% ± 0.05% at 24 h). B cells were 12.43% ± 0.10% at 24 h. Additionally, 10–20% of GFP+ leukocytes did not belong to the aforementioned cell types, and further investigation revealed their identity as CD4+ T cells. Data not only confirm previous findings of Leishmania infection with PBD leukocytes and association with B cells but also suggest that CD4+ T cells might influence the early-stage of infection.

Transcriptomic correlates of state modulation in GABAergic interneurons: A cross-species analysis

GABAergic inhibitory interneurons comprise many subtypes that differ in their molecular, anatomical and functional properties. In mouse visual cortex, they also differ in their modulation with an animal’s behavioural state, and this state modulation can be predicted from the first principal component (PC) of the gene expression matrix. Here, we ask whether this link between transcriptome and state-dependent processing generalises across species. To this end, we analysed seven single-cell and single-nucleus RNA sequencing datasets from mouse, human, songbird, and turtle forebrains. Despite homology at the level of cell types, we found clear differences between transcriptomic PCs, with greater dissimilarities between evolutionarily distant species. These dissim­ilarities arise from two factors: divergence in gene expression within homologous cell types and divergence in cell type abundance. We also compare the expression of cholinergic receptors, which are thought to causally link transcriptome and state modulation. Several cholinergic receptors predictive of state modulation in mouse interneurons are differentially expressed between species. Circuit modelling and mathematical analyses suggest conditions under which these expression differences could translate into functional differences.Significance StatementThe brain is a complex network of many different cell types. A particularly diverse group of cells is that of inhibitory interneurons, named for their suppressive effect. These interneurons change their activity depending on an animal’s behavioural state—at least in mice. Here, we investigate if this finding generalises to other species by comparing gene expression patterns of human, turtle, and zebra finch interneurons. Despite sharing an evolutionary past, we find that only human and mouse interneurons have similar gene expression patterns associated with state modulation. A mathematical model suggests which expression differences in individual cells translate into functional differences at the network level.

A Multi-Sized Unit Cell Method for the Design of LPBF Lattice Support Structures Concerning Complex Geometries

Abstract Composed of individual unit cells strategically arranged to achieve a desired function, lattices are a promising solution for laser powder bed fusion (LPBF) support structure design in additive manufacturing. Despite their many advantages (e.g., multifunctionality and reduced material cost), prior work in lattice support structure design primarily focuses on horizontal support domains that are not translatable to support domains for complex geometries, thereby limiting their application. This work introduces a multi-sized unit cell design optimization (MSO) method to create lattice support structures (LSS) for parts with complex geometries. The proposed method utilizes voxelization to generate LSS using box-like unit cells of different sizes. It also allows for efficient, high-dimensional design optimization for the types and locations of user-specified unit cells through a modified simulated annealing-based optimization algorithm. The effectiveness and efficiency of the MSO method are demonstrated through the case study of an adapter pipe for a high-temperature heat exchanger. For this demonstration, LSS using multi-sized unit cells are designed to increase heat transfer rate while satisfying structural integrity and material cost constraints. The case study results indicate that the design of the LSS derived from the MSO method fulfills all constraints, including the design constraint of 50% material cost reduction, compared to the solid support structure. In contrast, the lattice support structure designs derived from equal-sized unit cell methods either cannot satisfy all design constraints or have a lower heat transfer rate than the design of the MSO method.

Recent progress and applications of single-cell sequencing technology in breast cancer

Single-cell RNA sequencing (scRNA-seq) technology enables the precise analysis of individual cell transcripts with high sensitivity and throughput. When integrated with multiomics technologies, scRNA-seq significantly enhances the understanding of cellular diversity, particularly within the tumor microenvironment. Similarly, single-cell DNA sequencing has emerged as a powerful tool in cancer research, offering unparalleled insights into the genetic heterogeneity and evolution of tumors. In the context of breast cancer, this technology holds substantial promise for decoding the intricate genomic landscape that drives disease progression, treatment resistance, and metastasis. By unraveling the complexities of tumor biology at a granular level, single-cell DNA sequencing provides a pathway to advancing our comprehension of breast cancer and improving patient outcomes through personalized therapeutic interventions. As single-cell sequencing technology continues to evolve and integrate into clinical practice, its application is poised to revolutionize the diagnosis, prognosis, and treatment strategies for breast cancer. This review explores the potential of single-cell sequencing technology to deepen our understanding of breast cancer, highlighting key approaches, recent advancements, and the role of the tumor microenvironment in disease plasticity. Additionally, the review discusses the impact of single-cell sequencing in paving the way for the development of personalized therapies.

Mouse blood cells types and aging prediction using penalized Latent Dirichlet Allocation

BackgroundAging is a complex, heterogeneous process that has multiple causes. Knowledge on genomic, epigenomic and transcriptomic changes during the aging process shed light on understanding the aging mechanism. A recent breakthrough in biotechnology, single cell RNAseq, is revolutionizing aging study by providing gene expression profile of the entire transcriptome of individual cells. Many interesting information could be inferred from this new type of data with the help of novel computational methods.ResultsIn this manuscript a novel statistical method, penalized Latent Dirichlet Allocation (pLDA), is applied to an aging mouse blood scRNA-seq data set. A pipeline is built for cell type and aging prediction. The sequence of models in the pipeline take scRNA-seq expression counts as input, preprocess the data using pLDA and predict the cell type and aging status.ConclusionspLDA learns a dimension reduced representation of the expression profile. This representation allows identification of cell types and has predictability of the age of cells.

The role of GDF15 in attenuating noise-induced hidden hearing loss by alleviating oxidative stress

Noise-induced hidden hearing loss (HHL) is a newly uncovered form of hearing impairment that causes hidden damage to the cochlea. Patients with HHL do not have significant abnormalities in their hearing thresholds, but they experience impaired speech recognition in noisy environments. However, the mechanisms underlying HHL remain unclear. In this study, we developed single-cell transcriptome profiles of the cochlea of mice with HHL, detailing changes in individual cell types. Our study revealed a transient threshold shift, reduced auditory brainstem response wave I amplitude, and decreased number of ribbon synapses in HHL mice. Our findings suggest elevated oxidative stress and GDF15 expression in cochlear hair cells of HHL mice. Notably, the upregulation of GDF15 attenuated oxidative stress and auditory impairment in the cochlea of HHL mice. This suggests that a therapeutic strategy targeting GDF15 may be efficacious against HHL.Graphical HHL mice had a transient threshold shift, reduced ABR wave I amplitude, and decreased number of ribbon synapses.HHL mice's cochlear hair cells exhibited increased oxidative stress and elevated GDF15 expression.Upregulation of GDF15 attenuated oxidative stress and auditory damage in the cochlea of HHL mice, implying that GDF15-targeted treatment techniques may be useful for HHL.

Optimal estimation of SoC, SoH using machine learning models and design of control algorithm for active cell balancing in lithium ion batteries

Abstract Electric vehicles transform the automotive industry by replacing
traditional vehicles powered by fossil fuels with less polluting and efficient vehicles.
They are powered by rechargeable Li-ion batteries. While there are drawbacks
associated with Li-ion battery technology, it’s important to note that these challenges
can be addressed or mitigated effectively. A battery management system ensures the
tracking of all functions performed by the battery. An advanced battery management
system should accurately estimate the battery’s state of health and state of charge.
This paper aims to develop machine learning models for the estimation of the state of
health and state of charge and to implement cell balancing in a battery management
system. A dataset of battery charging and discharging profiles was used to train
various machine learning models for estimation. These methods are computationally
less complex than conventional methods. In addition, a cell balancing algorithm is
implemented to control the charging and discharging of individual cells in a battery
pack and balance the state of charge of each cell. The machine learning solution is
created using several machine learning models, including Gradient Boosting, Random
Forest, Linear Regression, AdaBoost, and Multi-layer Perceptron. Among the models

Gradient Boosting and Random Forest provide good MSE and R2 score. The use of
machine learning algorithms for the assessment of state of health and charge estimation
combined with the design of an efficient control algorithm for active cell balancing
offers significant advancements in the battery management system to optimise the
performance, reliability, and useful life of Li-ion batteries. The paper also presents
a case study utilising a combination of deep learning-based SoC, SoH estimation
algorithms in a simulated data set. A well-designed control algorithm for active
cell balancing presents a holistic and effective approach to optimise Li-ion batteries' performance,
reliability, and lifespan.

Single-Cell Transcriptomics Applied in Plants

Single-cell RNA sequencing (scRNA-seq) is a high-tech method for characterizing the expression patterns of heterogeneous cells in the same tissue and has changed our evaluation of biological systems by increasing the number of individual cells analyzed. However, the full potential of scRNA-seq, particularly in plant science, has not yet been elucidated. To explore the utilization of scRNA-seq technology in plants, we firstly conducted a comprehensive review of significant scRNA-seq findings in the past few years. Secondly, we introduced the research and applications of scRNA-seq technology to plant tissues in recent years, primarily focusing on model plants, crops, and wood. We then offered five databases that could facilitate the identification of distinct expression marker genes for various cell types. Finally, we analyzed the potential problems, challenges, and directions for applying scRNA-seq in plants, with the aim of providing a theoretical foundation for the better use of this technique in future plant research.

Connecting high-resolution 3D chromatin maps with cell division and cell differentiation at the root apical meristem

Key messageWe used marker-free technologies to study chromatin at cellular resolution. Our results show asymmetric chromatin distribution, explore chromatin dynamics during mitosis, and reveal structural differences between trichoblast and atrichoblast cell.The shapes, sizes, and structural organizations of plant nuclei vary considerably among cell types, tissues, and species. This diversity is dependent on various factors, including cellular function, developmental stage, and environmental or physiological conditions. The differences in nuclear structure reflect the state of chromatin, which, in turn, controls gene expression and regulates cell fate. To examine the interrelationship between nuclear structure, cell morphology, and tissue-specific cell proliferation and differentiation processes, we conducted multiple visualizations of H3K4me1, H3K9me2, 4′,6-diamidino-2-phenylindole, 5-ethynyl 2′-deoxyuridine, and SCRI Renaissance 2200, followed by subsequent quantitative analysis of individual cells and nuclei. By assigning cylindrical coordinates to the nuclei in the iRoCS toolbox, we were able to construct in situ digital three-dimensional chromatin maps for all the tissue layers of individual roots. A detailed analysis of the nuclei features of H3K4me1 and H3K9me2 in the mitotic and the elongation zones in trichoblast and atrichoblast cells at the root apical meristem revealed cell type-specific chromatin dynamics with asymmetric distribution of euchromatin and heterochromatin marks that may be associated with cell cycle and cell differentiation characteristics of specific cells. Furthermore, the spatial distribution of nuclei stained with 5-ethynyl 2′-deoxyuridine in the epidermis and cortex tissues suggests short-range coordination of cell division and nuclear migration in a linear sequence through an unknown regulatory mechanism.

Large-volume fully automated cell reconstruction generates a cell atlas of plant tissues.

The geometric shape and arrangement of individual cells play a role in shaping organ functions. However, analyzing multicellular features and exploring their connectomes in centimeter-scale plant organs remain challenging. Here, we established a set of frameworks named Large-Volume Fully Automated Cell Reconstruction (LVACR), enabling the exploration of three-dimensional (3D) cytological features and cellular connectivity in plant tissues. Through benchmark testing, our framework demonstrated superior efficiency in cell segmentation and aggregation, successfully addressing the inherent challenges posed by light sheet fluorescence microscopy (LSFM) imaging. Using LVACR, we successfully established a cell atlas of different plant tissues. Cellular morphology analysis revealed differences of cell clusters and shapes in between different poplar (P. simonii Carr. and P. canadensis Moench.) seeds, whereas topological analysis revealed that they maintained conserved cellular connectivity. Furthermore, LVACR spatiotemporally demonstrated an initial burst of cell proliferation, accompanied by morphological transformations at an early stage in developing the shoot apical meristem. During subsequent development, cell differentiation produced anisotropic features, thereby resulting in various cell shapes. Overall, our findings provided valuable insights into the precise spatial arrangement and cellular behavior of multicellular organisms, thus enhancing our understanding of the complex processes underlying plant growth and differentiation.

Functional, patient-derived 3D tri-culture models of the uterine wall in a microfluidic array.

Can a functional in vitro model, containing the main cellular components of the uterine wall, be generated from cells derived from patient tissues? We present a three-dimensional (3D) physiologically relevant, organ-on-a-chip model of the uterine wall containing primary endometrial and myometrial cellular participants, generated from human uterine tissue. As a highly dynamic reproductive organ, the human uterus plays fundamental physiological roles in menstruation and childbirth. The endometrial-myometrial junction (EMJ) defines the interface between the inner mucosal layer (endometrium) and outer smooth muscle zone (myometrium) that comprises the uterine wall. The EMJ is implicit in several uterine pathologies of unknown aetiology, including adenomyosis and abnormally invasive placenta; however, despite this, no patient-derived in vitro models of the uterine wall containing all EMJ participants currently exist. We employed microfluidic technology to characterize multiple miniaturized models of the uterine wall. Protocols were tested that included variations in the seeding order of endometrial and myometrial fractions, and the addition of a low viscosity extracellular matrix to influence cell behaviour. Ultimately, functional hormone responses of patient-derived uterine wall models were assessed. Endometrial (n = 9) and myometrial biopsies (n = 4) were enzymatically dissociated to create epithelial, stromal and myometrial cellular fractions. Cell suspensions were seeded into non-adhesive poly(dimethylsiloxane) microfluidic devices containing 5 × 5 microwell arrays. The fate of individual cell types was monitored in real-time using fluorescent tracers, and cell phenotype was characterized by immunocytochemistry. Model functionality was assessed by measuring Ca2+ responses to agonist stimulation, and both insulin-like growth factor binding protein 1 (IGFBP-1) and osteopontin secretion in response to hormone stimulation. When subjected to microfluidic culture in isolation, endometrial stromal cells and smooth muscle myocytes formed compact spheroids, whilst epithelial cells produced diffuse aggregates. Tri-cultures were established by sequential seeding of individual or combined cell fractions at various ratios. Regardless of the protocol, epithelial cells localized to the outer periphery of tri-culture spheroids, which varied in morphology across the protocols. Incorporation of 5% [v/v] Matrigel® improved the reproducibility of 3D aggregates which exhibited robust self-assembly of a stromal/smooth muscle core encased in epithelium. Exposure of tri-cultures to oestradiol, medroxyprogesterone acetate and cyclic adenosine monophosphate (cAMP) increased secretion of IGFBP-1, which indicates stromal decidualization, and enhanced epithelial cell osteopontin secretion. Stimulation with endothelin-1 induced Ca2+ signalling in myocytes. Endometrial and myometrial tissue was collected from relatively few donors. Myometrial tissue was collected from pregnant donors, which may have influenced the myocyte phenotype. Furthermore, endometrial tissue sampling was from women not having a hysterectomy, thus may not include the deeper basalis region, which may limit the physiological mimicry of the final models. Our novel approach to modelling the uterine wall in 3D captures all of the main cell types in a medium-throughput system, enabling the screening of hundreds of cultures in parallel from a single biopsy. This system shows great promise for examining the cellular interplay between physiological cues and EMJ pathologies, such as the impact of uterine peristalsis and cyclical hormones on the pathogenesis of adenomyosis. C.B. was supported by an Organ-on-a-Chip Technologies Network Pump Priming Project grant. C.J.H. was supported by a Wellbeing of Women project grant (RG2137), SRI/Bayer and Wellcome Trust IFFS3. D.K.H. was supported by a Wellbeing of Women project grant (RG2137) and MRC clinical research training fellowship (MR/V007238/1). M.Z. is Director and Co-Founder of ScreenIn3D Limited. The other authors declare no conflict of interest. N/A.

Anatomical and Physiological Perspectives of Various Srotas: A Comprehensive Review

Abstract: Background: In Ayurveda, Srotas are fundamental components of the body's transport system. Acharya describe numerous Srotas that facilitate the balance of Dosha, Dhatu, and Mala. These channels, which can vary in size from micro to macroscopic, are crucial for a range of physiological functions. Aim: To explore the anatomical and physiological aspects of Srotas in Ayurveda, focusing on their role in maintaining bodily balance and their impact on health and disease. Material & Methods: This review synthesizes information from classical Ayurvedic texts, such as the Charaka Samhita, to clarify the concept of Srotas. It explores their structural and functional roles, including absorption, secretion, transportation, drying, excretion, and separation. Discussion: The term 'Srotamsi' includes both Anusrotas( individual cells) and Sthula Srotas (organ systems), derived from the Sanskrit root 'Stru-Strabane,' indicating movement and filtration. Srotas are crucial for the flow of substances and significantly impact the state of Dosha, either aggravating or pacifying them. Proper functioning of Srotas is vital for maintaining health and restoring balance; dysfunction of Srotas can lead to various pathological conditions. Conclusion: A thorough understanding of Srotas from anatomical and physiological perspectives is essential for appreciating their role in health and disease. This review highlights the importance of maintaining Srotas functionality to prevent and address health issues in line with Ayurvedic principles.

Abstract A045: Disrupting local immunosuppression through combination myCAF/myeloid targeting in pancreatic cancer

Abstract Immunotherapy has revolutionized clinical care for many cancers, yet the approach has not been fully realized in immunologically cold tumors like Pancreatic Ductal Adenocarcinoma (PDAC). One striking hallmark of PDAC is local immunosuppression (LIS) which leads to cytotoxic T cell inactivation and exclusion. There is great need for an increased understanding of the cellular crosstalk within tumors and the identification of stromal and immune populations involved in shaping the tumor microenvironment (TME). Oncogenic KRAS activation in tumor cells promotes the invasion and proliferation of tumor-supporting stromal cells, while excluding cancer-targeted cytotoxic T cells. LIS is mediated by multiple subtypes of cancer-associated fibroblasts (CAFs) and myeloid cells resident within the tumor parenchyma. Multiple prior attempts to reverse LIS in PDAC by targeting individual stromal cell populations have been unsuccessful, alluding to the complexity of stromal crosstalk within the TME. The stromal diversity of PDAC complicates investigating paracrine cascades involving multiple cell types. To decipher diverse drug effects on altering the TME, we employ in vivo studies in mouse models recapitulating the human disease, as well as a novel tumor explant model that enables the short-term culture of intact human or murine PDAC. Importantly, PDAC explants maintain their histopathological architecture and cellular diversity over time. This medium-throughput platform allows for testing of multiple drugs and mechanistic hypotheses in the native PDAC TME. We show in preliminary data that Smoothened inhibition (SMOi) decreases proliferation and activity of myCAFs, but provokes the expansion of CD11b-positive myeloid cells in vivo. Thus, we hypothesize that LIS in PDAC is maintained by a delicate balance between myCAFs and myeloid cells, preventing effective T cell invasion. Single cell RNA-seq data comparing ctrl vs. SMOi-treated murine PDAC elucidates stromal subpopulations involved in the LIS phenotype and guides the identification of myeloid subtypes emerging after SMOi. Strikingly, we demonstrated that simultaneous SMOi and targeting myeloid cells via anti-Gr1 or CCR1 inhibition (CCR1i) significantly elevates cytotoxic T cell numbers up to 20x within the TME. We are currently investigating whether the activity of these T cells may be further potentiated through combination with immunomodulatory agents. By testing various treatment combination in the same TME, we will identify the best synergistic effects for future immunotherapy approaches in human PDAC. In summary, we are elucidating the complex mechanism behind LIS in PDAC by employing our novel explant culture system alongside in vivo studies. We aim to develop a translatable regimen to neutralize LIS, reactivating the cytotoxic T cells in the tumor periphery to invade, proliferate, and attack cancer cells. Citation Format: Marie C Hasselluhn, Lukas Vlahos, Dafydd Thomas, Quin T Waterbury, Alvaro Curiel Garcia, Amanda R Decker-Farrell, Tanner C Dalton, Stephen A Sastra, Carmine F Palermo, Timothy C Wang, Andrea Califano, Kenneth P Olive. Disrupting local immunosuppression through combination myCAF/myeloid targeting in pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr A045.

Abstract IA-07: RAS mutation tropism: insight into tumor initation

Abstract How we get cancer, a process termed tumor initiation, remains an enigma, which has crucial clinical implications for early detection, prevention, and interception. Backtracking to catch the moment a specific mutagenic event in a single gene from an individual cell occurred decades before manifesting as cancer is challenging. However, the RAS family of small GTPases thate are commonly mutated in cancer provide some insight. Despite over 50 possible oncogenic RAS mutations, each cancer type exhibits a predilection or ‘tropism’ towards a distinct and often unique subset of these mutations. For example, most pancreatic ductal adenocarcinomas are driven by a G12D/V mutation in KRAS. As RAS mutations are often initiating, this tropism ostensibly reflects the fundamental biology underlying tumor initiation, which becomes embedded in the genome of the resulting tumors. Fortunately, the process of RAS mutation tropism can be studied in mice exposed to the environmental carcinogen urethane. Such carcinogenesis both captures the moment of tumor initiation (the point of carcinogen exposure) and displays RAS mutation tropism (lung lesions are induced by a specific RAS mutation). By using ultra-sensitive sequencing to detect mutations immediately after urethane exposure in different mouse genetic backgrounds, we find that the mutagenic bias of this carcinogen influences the type of RAS mutations generated. However, biological selection for an optimal or ‘sweet spot’ of oncogenic signaling ultimately appears to dictate which mutations go on to initiate tumorigenesis. Analysis of sequence datasets from human lung cancer suggests a similar dominant role for biological selection in the RAS mutation tropism of this cancer. In summary, RAS mutation tropism appears to be a combination of mutational bias, and even more so, biological selection, providing critical insights into the origins of cancers like pancreatic. Citation Format: Christopher M Counter. RAS mutation tropism: insight into tumor initation [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr IA-07.

Benchmarking bulk and single-cell variant-calling approaches on Chromium scRNA-seq and scATAC-seq libraries.

Single-cell sequencing methodologies such as scRNA-seq and scATAC-seq have become widespread and effective tools to interrogate tissue composition. Increasingly, variant callers are being applied to these methodologies to resolve the genetic heterogeneity of a sample, especially in the case of detecting the clonal architecture of a tumor. Typically, traditional bulk DNA variant callers are applied to the pooled reads of a single-cell library to detect candidate mutations. Recently, multiple studies have applied such callers on reads from individual cells, with some citing the ability to detect rare variants with higher sensitivity. Many studies apply these two approaches to the Chromium (10x Genomics) scRNA-seq and scATAC-seq methodologies. However, Chromium-based libraries may offer additional challenges to variant calling compared with existing single-cell methodologies, raising questions regarding the validity of variants obtained from such a workflow. To determine the merits and challenges of various variant-calling approaches on Chromium scRNA-seq and scATAC-seq libraries, we use sample libraries with matched bulk whole-genome sequencing to evaluate the performance of callers. We review caller performance, finding that bulk callers applied on pooled reads significantly outperform individual-cell approaches. We also evaluate variants unique to scRNA-seq and scATAC-seq methodologies, finding patterns of noise but also potential capture of RNA-editing events. Finally, we review the notion that variant calling at the single-cell level can detect rare somatic variants, providing empirical results that suggest resolving such variants is infeasible in single-cell Chromium libraries.

ImputeHiFI: An Imputation Method for Multiplexed DNA FISH Data by Utilizing Single-Cell Hi-C and RNA FISH Data.

Although multiplexed DNA fluorescence in situ hybridization (FISH) enables tracking the spatial localization of thousands of genomic loci using probes within individual cells, the high rates of undetected probes impede the depiction of 3D chromosome structures. Current data imputation methods neither utilize single-cell Hi-C data, which elucidate 3D genome architectures using sequencing nor leverage multimodal RNA FISH data that reflect cell-type information, limiting the effectiveness of these methods in complex tissues such as the mouse brain. To this end, a novel multiplexed DNA FISH imputation method named ImputeHiFI is proposed, which fully utilizes the complementary structural information from single-cell Hi-C data and the cell type signature from RNA FISH data to obtain a high-fidelity and complete spatial location of chromatin loci. ImputeHiFI enhances cell clustering, compartment identification, and cell subtype detection at the single-cell level in the mouse brain. ImputeHiFI improves the recognition of cell-type-specific loops in three high-resolution datasets. In short, ImputeHiFI is a powerful tool capable of imputing multiplexed DNA FISH data from various resolutions and imaging protocols, facilitating studies of 3D genome structures and functions.

Characterization of Human Pancreatic Islet Cells using a Single-Cell Western Blot Platform.

Islet transplantation is an effective treatment for type 1 diabetes. However, transplant success depends on quick islet assessment because islets deteriorate 2-3 days after isolation. A new tool, single-cell Western blot (scWestern), offers results within one day. In this study, we aimed to test the suitability of scWestern to detect protein markers for beta (insulin), alpha (glucagon), and delta (somatostatin) cells, the three major endocrine cell types in islets. We characterized the antibody specificity, signal intensity, and cell identification on the scWestern platform, and then compared the islet cell composition analysis between scWestern and immunohistochemistry performed by the Integrated Islet Distribution Program (IIDP). Islet cell composition is comparable for alpha and beta cells, but not delta cells. Protein expression levels of insulin, glucagon, and somatostatin in individual islet cells varied greatly, highlighting cell type heterogeneity. Surprisingly, scWestern revealed double-hormonal cells (~1%), co-expressing insulin and somatostatin or insulin and glucagon, in non-diabetic and non-obese adult human islets, which was confirmed by confocal immunofluorescence microscopy. These results demonstrate that each alpha, beta, and delta cells express varying levels of peptide hormones, and a small subpopulation co-expresses double hormones in normal human islets. The scWestern platform will enable timely assessment of beta cell mass in isolated islets before clinical transplantation.

Numerical integration of mechanical forces in center-based models for biological cell populations

Center-based models are used to simulate the mechanical behavior of biological cells during embryonic development or cancer growth. To allow for the simulation of biological populations potentially growing from a few individual cells to many thousands or more, these models have to be numerically efficient, while being reasonably accurate on the level of individual cell trajectories. In this work, we increase the robustness, accuracy, and efficiency of the simulation of center-based models by choosing the time steps adaptively in the numerical method and comparing five different integration methods. We investigate the gain in using single rate time stepping based on local estimates of the numerical errors for the forward and backward Euler methods of first order accuracy and a Runge-Kutta method and the trapezoidal method of second order accuracy. Properties of the analytical solution such as convergence to steady state and conservation of the center of gravity are inherited by the numerical solutions. Furthermore, we propose a multirate time stepping scheme that simulates regions with high local force gradients (e.g. as they happen after cell division) with multiple smaller time steps within a larger single time step for regions with smoother forces. These methods are compared for a model system in numerical experiments. We conclude, for example, that the multirate forward Euler method performs better than the Runge-Kutta method for low accuracy requirements but for higher accuracy the latter method is preferred. Only with frequent cell divisions the method with a fixed time step is the best choice.