Complex Biological Systems Research Articles

Monitoring of mercury ion in environmental media and biological systems using a red emissive fluorescent probe with a large Stokes shift

The development of practical fluorescent probe for detecting toxic mercury ions (Hg2+) is desirable for environmental assurance and public health. In this study, a new red emissive fluorescent probe (KJL) was designed and synthesized for monitoring trace Hg2+ both in vitro and in vivo with distinct features including ideal response rate (within 4 min), red emission (596 nm), large Stokes shift (162 nm), highly sensitivity (LOD = 4.79 nM) and excellent specificity. KJL also validated the good capability for accurately monitoring trace Hg2+ levels in actual samples (faucet water, drinking water, river water, lake water, urine and serum) and possessed the eye-catching ability in visualization of Hg2+ under environmental/biological conditions, which revealed the great potential of this red-emitting fluorescent probe for practical applications in complex environmental and biological systems.

Thermostatted Kinetic Theory Structures in Biophysics: Generalizations and Perspectives

The mathematical modeling of multicellular systems is an important branch of biophysics, which focuses on how the system properties emerge from the elementary interaction between the constituent elements. Recently, mathematical structures have been proposed within the thermostatted kinetic theory for the modeling of complex living systems and have been profitably employed for the modeling of various complex biological systems at the cellular scale. This paper deals with a class of generalized thermostatted kinetic theory frameworks that can stand in as background paradigms for the derivation of specific models in biophysics. Specifically, the fundamental homogeneous thermostatted kinetic theory structures of the recent literature are recovered and generalized in order to take into consideration further phenomena in biology. The generalizations concern the conservative, the nonconservative, and the mutative interactions between the inner system and the outer environment. In order to sustain the strength of the new structures, some specific models of the literature are reset into the style of the new frameworks of the thermostatted kinetic theory. The selected models deal with breast cancer, genetic mutations, immune system response, and skin fibrosis. Future research directions from the theoretical and modeling viewpoints are discussed in the whole paper and are mainly devoted to the well-posedness in the Hadamard sense of the related initial boundary value problems, to the spatial–velocity dynamics and to the derivation of macroscopic-scale dynamics.

TRANSFORMING HEALTHCARE DELIVERY THROUGH BIG DATA IN HOSPITAL MANAGEMENT SYSTEMS: A REVIEW OF RECENT LITERATURE TRENDS

This systematic review investigates the transformative role of big data technologies in biomedical research, analyzing 40 peer-reviewed articles published between 2010 and 2023. The review specifically explores advancements in next-generation sequencing (NGS), multi-omics approaches, machine learning, and artificial intelligence (AI), all of which have significantly enhanced the understanding of complex biological systems and diseases. NGS has emerged as a key tool in personalized medicine, enabling rapid and cost-effective genome sequencing that has facilitated the identification of genetic mutations and biomarkers associated with various diseases, particularly in oncology. Of the 40 studies reviewed, 12 focused on the integration of multi-omics data—genomics, transcriptomics, proteomics, and metabolomics—to provide a comprehensive view of biological processes. These multi-omics approaches have been instrumental in identifying biomarkers for disease progression and response to treatments, offering new avenues for drug development and precision medicine. Additionally, 15 studies highlighted the growing application of machine learning and AI algorithms in managing and analyzing vast biomedical datasets. These tools are now critical in uncovering hidden patterns within large datasets, predicting disease outcomes, and improving the accuracy of clinical decision-making. However, 10 studies emphasized ongoing challenges related to data storage, privacy concerns, and the lack of standardized data formats, which hinder effective data sharing across institutions. Despite these challenges, the integration of AI, IoT devices, and big data analytics is paving the way for more personalized, real-time healthcare monitoring and treatment solutions. This review concludes that while significant advancements have been made, further efforts are required to address the ethical and technical barriers that limit the full potential of big data technologies in biomedical research.

Lifetime minimally invasive assessment of iron in the pig liver

Microelements are an integral part of the mammalian body, and their content in organs and tissues is associated with other components of a complex biological system. Based on this, it is feasible to evaluate the concentration of specific chemical elements within the structures of the body in non-invasive or minimally invasive methods. The meat and by-products of farm animals serve as a readily assimilateable source of iron, which is one of the reasons for potentially defining the quality of agricultural products in conditions of widespread iron deficiency. Landrace pigs were raised in standard conditions at an industrial complex located in the Altai Territory in order to fatten up to a live weight of 100 kg. Venous blood was collected using the acute method from the jugular vein in accordance with the principles of asepsis and pre-analytical guidelines. The hematologic and biochemical examination of the blood and serum of animals was performed by apparatus. After slaughter, liver samples were collected, and the method of atomic emission spectral analysis using inductively coupled plasma on iCAP-PRO equipment (Thermo Fisher Scientific) was used to estimate the iron level in them. To manipulate the data, Microsoft Office Excel software and RStudio data analysis environment version 2023.03.1 (RStudio, PBC) were employed. For regression analysis, the least squares approach was used. The model was fitted using a stepwise selection of predictors in both directions using the Akaike information criterion, Bayesian information criterion, and adjusted coefficient of determination. The linear regression assumptions were evaluated. The final regression model used for determining iron levels in pig liver contains mean hemoglobin content in erythrocytes, hemoglobin, and serum inorganic phosphorus as predictors. There is no evidence that there is multicollinearity between the predictors of the final model. The proposed model satisfies the requirements for a normal distribution of residuals, the absence of their correlation, and influential observations. The proposed multiple regression model has the capability to estimate iron levels in pig liver in vivo for various purposes.

Constrained nuclear-electronic orbital QM/MM approach for simulating complex systems with quantum nuclear delocalization effects incorporated

The hybrid quantum mechanics/molecular mechanics (QM/MM) approach, which combines the accuracy of QM methods with the efficiency of MM methods, is widely used in the study of complex systems. However, past QM/MM implementations often neglect or face challenges in addressing nuclear quantum effects, despite their crucial role in many key chemical and biological processes. Recently, our group developed the constrained nuclear-electronic orbital (CNEO) theory, a cost-efficient approach that accurately addresses nuclear quantum effects, especially quantum nuclear delocalization effects. In this work, we integrate CNEO with the QM/MM approach through the electrostatic embedding scheme and apply the resulting CNEO QM/MM to two hydrogen-bonded complexes. We find that both solvation effects and nuclear quantum effects significantly impact hydrogen bond structures and dynamics. Notably, in the glutamic acid–glutamate complex, which mimics a common low barrier hydrogen bond in biological systems, CNEO QM/MM accurately predicts nearly equal proton sharing between the two residues. With an accurate description of both quantum nuclear delocalization effects and environmental effects, CNEO QM/MM is a promising new approach for simulating complex chemical and biological systems.

Neuroscientific insights about computer vision models: a concise review.

The development of biologically-inspired computational models has been the focus of study ever since the artificial neuron was introduced by McCulloch and Pitts in 1943. However, a scrutiny of literature reveals that most attempts to replicate the highly efficient and complex biological visual system have been futile or have met with limited success. The recent state-of the-art computer vision models, such as pre-trained deep neural networks and vision transformers, may not be biologically inspired per se. Nevertheless, certain aspects of biological vision are still found embedded, knowingly or unknowingly, in the architecture and functioning of these models. This paper explores several principles related to visual neuroscience and the biological visual pathway that resonate, in some manner, in the architectural design and functioning of contemporary computer vision models. The findings of this survey can provide useful insights for building futuristic bio-inspired computer vision models. The survey is conducted from a historical perspective, tracing the biological connections of computer vision models starting with the basic artificial neuron to modern technologies such as deep convolutional neural network (CNN) and spiking neural networks (SNN). One spotlight of the survey is a discussion on biologically plausible neural networks and bio-inspired unsupervised learning mechanisms adapted for computer vision tasks in recent times.

Deep learning pipeline for automated cell profiling from cyclic imaging

Cyclic fluorescence microscopy enables multiple targets to be detected simultaneously. This, in turn, has deepened our understanding of tissue composition, cell-to-cell interactions, and cell signaling. Unfortunately, analysis of these datasets can be time-prohibitive due to the sheer volume of data. In this paper, we present CycloNET, a computational pipeline tailored for analyzing raw fluorescent images obtained through cyclic immunofluorescence. The automated pipeline pre-processes raw image files, quickly corrects for translation errors between imaging cycles, and leverages a pre-trained neural network to segment individual cells and generate single-cell molecular profiles. We applied CycloNET to a dataset of 22 human samples from head and neck squamous cell carcinoma patients and trained a neural network to segment immune cells. CycloNET efficiently processed a large-scale dataset (17 fields of view per cycle and 13 staining cycles per specimen) in 10 min, delivering insights at the single-cell resolution and facilitating the identification of rare immune cell clusters. We expect that this rapid pipeline will serve as a powerful tool to understand complex biological systems at the cellular level, with the potential to facilitate breakthroughs in areas such as developmental biology, disease pathology, and personalized medicine.

Rational Molecular Design of a Fluorescent Probe for Selectively Sensing Human Cytochrome P450 2D6.

Cytochrome P450 2D6 (CYP2D6) is a key enzyme that mediates the metabolism of various drugs and endogenous substances in humans. However, its biological role in drug-drug interactions especially mechanism-based inactivation (MBI), and various diseases remains poorly understood, owing to the lack of molecular tools suitable for selectively monitoring CYP2D6 in complex biological systems. Herein, using a tailored molecular strategy, we developed a fluorescent probe BDPM for CYP2D6. BDPM exhibits excellent specificity and imaging capability for CYP2D6, making it suitable for the real-time monitoring of endogenous CYP2D6 activity in living bio-samples. Therefore, our tailored strategy proved useful for constructing the highly selective and enzyme-activated fluorescent probes. BDPM as a molecular tool to explore the critical roles of CYP2D6 in the pathogenesis of diseases, high-throughput screening of inhibitors and intensive investigation of CYP2D6-induced MBI in natural systems.

Covalent Labeling of Matrix Metalloproteases with Affinity-Based Probes Containing Tuned Reactive N-Acyl-N-Alkyl Sulfonamide Cleavable Linkers.

Original covalent probes with an N-acyl-N-alkyl sulfonamide cleavable linker were developed to target a broad set of human Matrix Metalloproteases (MMPs). The electrophilicity of this cleavable linker was modulated to improve the selectivity of the probes as well as reduce their unspecific reactivity in complex biological matrices. We first demonstrated that targeting the S3 subsite of MMPs enables access to broad-spectrum affinity-based probes that exclusively react with the active version of these proteases. The probes were further assessed in proteomes of varying complexity, where human MMP-13 was artificially introduced at known concentration and the resulting labeled MMP was imaged by in-gel fluorescence imaging. We showed that the less reactive probe was still able to covalently modify MMP-13 while exhibiting reduced off-target unspecific reactivity. This study clearly demonstrated the importance of finely controlling the reactivity of the NASA warhead to improve the selectivity of covalent probes in complex biological systems.

Mass Spectrometry–Based Proteomics for the Masses: Peptide and Protein Identification in the Hunt Laboratory During the 2000’s

There has been a rapid increase in the number of individuals utilizing mass spectrometry (MS)-based proteomics to study complex biological systems and questions since the start of the 2000's. Building off the advancements in ionization and liquid chromatography scientists continued to push towards technology that would enable in-depth analysis of biological specimen. Donald F Hunt and the Hunt laboratory were major contributors to this effort with their work on improving upon existing Fourier Transform MS, development of electron transfer dissociation, and continued work on ion-ion reactions to improve intact protein analysis. Collaboration with other instrumentation laboratories and instrument companies led to the sharing of technology and eventual commercialization providing greater access. Additionally, the Hunt laboratory spread the gospel of mass spectrometry-based proteomics through collaborations that lasted decades with other scientists who were experts in immunology, cellular signaling, epigenetics, and other fascinating fields. This article attempts to highlight the many contributions of Don and the Hunt laboratory to peptide and protein identification since the year 2000.

An integrated In-Silico-In-Chemico-In-Vitro (iSiCiV) Approach to identify biomarkers to predict the skin sensitisation potential of phytochemicals

BackgroundSkin sensitisation is one of the important regulatory endpoints used to determine whether a given test chemical can elicit an allergic response in susceptible individuals. The in-vitro approaches for predicting skin sensitisation potential for chemicals have been well-developed and widely accepted. Continuous efforts are being made to identify novel solutions to overcome the limitations of the existing approaches. PurposeThe herbal preparations used in dermal therapeutics are frequently found to elicit skin sensitisation, requiring caution. The herbal preparations tend to contain more than one phytochemical. The expected active ingredient may not possess skin sensitisation. However, the other phytochemical traces could pose synergistic and potentiating effects towards enhanced/suppressed skin sensitisation. In this regard, isolation, purification, yield, and in-vitro / in-vivo evaluation of skin sensitisation for all the traces would be time, cost, and animal-consuming. Hence, the regulatory agencies appreciate using computational tools to substantiate in-vitro methods. The regulators have also endorsed using in-silico approaches like quantitative structure-activity relationships (QSAR) and read-across. These approaches are limited to structure-based predictions and in combination with system-level approaches, which comprehensively facilitate a better understanding of complex biological systems. Study designIn the present study, 603 phytochemicals were screened in-silico using OECD QSAR Toolbox. The limitations of the QSAR Toolbox in predicting skin sensitisers were identified. MethodThe systems biology-based approach to complement the QSAR-based prediction was developed, and 41 biomarkers were identified as a determinant of skin sensitisation. The molecules attributed to these biomarkers were predicted as skin sensitisers. ResultsAs a result, 31 phytochemicals were predicted as skin sensitisers. Acrolein, eugenol, formaldehyde, and glycerol were selected for experimental validation of the in-silico predictions using direct peptide reactivity assay (DPRA), KeratinoSens™, and h-CLAT assays representing each key event in skin sensitisation. Further, identified biomarkers were validated in THP-1 and KeratinoSens cell lines using qRT-PCR. Among all genes, IL-8 and CCL4 were upregulated by 6.4 and 5.6 folds, respectively, in THP-1, while AKR1C2 and AKR1C3 were upregulated by 7.8 and 11.1 folds, respectively, in KeratinoSens. IL-8 level was analysed by ELISA and found to be in line with qRT-PCR results. ConclusionThe study proposes a possible strategy based on the “iSiCiV” approach to enhance the prediction of the dermal sensitisation potential of a test compound. The approach offers a robust platform and highlights an opportunity to shift the paradigm of skin sensitisation assay with a better understanding of toxicology mechanisms.

Utilising an in silico model to predict outcomes in senescence-driven acute liver injury

Currently liver transplantation is the only treatment option for liver disease, but organ availability cannot meet patient demand. Alternative regenerative therapies, including cell transplantation, aim to modulate the injured microenvironment from inflammation and scarring towards regeneration. The complexity of the liver injury response makes it challenging to identify suitable therapeutic targets when relying on experimental approaches alone. Therefore, we adopted a combined in vivo-in silico approach and developed an ordinary differential equation model of acute liver disease able to predict the host response to injury and potential interventions. The Mdm2fl/fl mouse model of senescence-driven liver injury was used to generate a quantitative dynamic characterisation of the key cellular players (macrophages, endothelial cells, myofibroblasts) and extra cellular matrix involved in liver injury. This was qualitatively captured by the mathematical model. The mathematical model was then used to predict injury outcomes in response to milder and more severe levels of senescence-induced liver injury and validated with experimental in vivo data. In silico experiments using the validated model were then performed to interrogate potential approaches to enhance regeneration. These predicted that increasing the rate of macrophage phenotypic switch or increasing the number of pro-regenerative macrophages in the system will accelerate the rate of senescent cell clearance and resolution. These results showcase the potential benefits of mechanistic mathematical modelling for capturing the dynamics of complex biological systems and identifying therapeutic interventions that may enhance our understanding of injury-repair mechanisms and reduce translational bottlenecks.

An economically viable stable isotope-enhanced multiple reaction monitoring method for total fatty acid analysis in a mouse model of non-alcoholic fatty liver disease

The complex pathological mechanisms of non-alcoholic fatty liver disease (NAFLD) are closely related to dysregulated lipid metabolism, and the therapeutic effects of the traditional Chinese medicine Zexie-Baizhu Decoction (AA) on NAFLD have been gaining increasing attention. However, research into altered lipid metabolism, especially fatty acids, in NAFLD and the intervention of AA faces technical challenges, especially in the precise quantitative analysis of fatty acids in biological samples. The high complexity of biological matrices, particularly after drug intervention, greatly increases the difficulty of detection. Therefore, this study innovatively developed a simple and economical stable isotope derivatization technique by synthesizing d6N,N-dimethylethylenediamine (d6-DMED) in the laboratory, establishing a simple and economical method for fatty acid quantification. This method employs a chemical reaction under low-temperature conditions to ensure the efficient synthesis of d6-DMED. Using ultra-high performance liquid chromatography-triple quadrupole mass spectrometry technique (UHPLC-MS/MS), combined with optimized chromatographic separation conditions and dynamic multiple reaction monitoring mode, the study established a highly sensitive detection method for 35 fatty acid derivatives. Methodological evaluation showed that the limits of quantification ranged from 0.002 to 0.060 μM, with high linearity of R² > 0.995. Additionally, the relative recovery rates were between 93.14% and 106.63%. To further demonstrate the feasibility of this method for fatty acid quantification, it was applied to measure fatty acids in multiple tissues in a mouse NAFLD model, as well as the effects of AA intervention on fatty acid metabolism. This rapid, simple, and cost-effective detection method not only enhances the understanding of NAFLD mechanisms but also provides a new strategy for evaluating the biological complex system after drug intervention.

Molecular Imaging of Human Brain Organoids Using Mass Spectrometry.

Brain organoid models serve as a powerful tool for studying human brain development and function. Mass spectrometry imaging (MSI), a cutting-edge technology, allows us to map the spatial distribution of diverse molecules such as lipids, sugars, amino acids, drugs, and their metabolites within these organoids, all without the need for specific molecular probes. High-quality MSI data hinge on meticulous sample preparation. Fixatives play a pivotal role, but conventional options such as glutaraldehyde, paraformaldehyde, and cryopreserving such as sucrose may inadvertently impact tissue metabolites. Optimal fixation entails flash freezing in liquid nitrogen. However, for small organoids, a more suitable approach involves transitioning the organoids directly from the incubator into a warmed embedding solution, followed by freezing in dry ice-cooled ethanol. Another critical step is the embedding prior to cryosectioning, which also requires materials compatible with MSI, as traditional options can interfere with matrix deposition and ionization. Here, an optimized protocol for high resolution-MALDI-MSI of human brain organoids is presented, encompassing sample preparation, sectioning, and imaging using mass spectrometry. This method showcases the molecular distribution of small metabolites, such as amino acids, with high mass accuracy and sensitivity. As such, coupled with complementary studies of brain organoids, it can assist in illuminating complex processes governing early brain development, metabolic cell fate trajectories, and distinctive metabolite signatures. Furthermore, it provides insights into the precise locations of molecules within the organoid, enriching our understanding of the spatial organization of 3D brain organoid models. As the field continues to advance, a growing number of studies leveraging MSI to delve into brain organoids and complex biological systems is anticipated, thereby deepening the understanding of the metabolic aspects of human brain function and development.

Multienzymatic Orthogonal Activation of DNA Codec Enables Tumor-Specific Imaging of Base Excision Repair Activity.

Accurate monitoring of base excision repair (BER) activity in cancer cells is critical for advancing the comprehension of DNA repair processes, gaining insights into cancer development, and guiding treatment strategies. However, current assay techniques for assessing BER activity in cancer cells face challenges due to the heterogeneous origins and diversity of BER enzymes. In this work, we present a highly reliable triple loop-interlocked DNA codec (GATED) that enables precise assessment of BER activity in cancer cells through signal amplification mediated by multienzyme orthogonal activation. The GATED device features a dumbbell-shaped DNA probe to encode two BER enzymes for BER-related signal conversion as well as two bound circular DNA to decode the apurinic/apyrimidinic sites for apurinic/apyrimidinic endonuclease 1 (APE1)-mediated signal amplification. Importantly, GATED is orthogonally activated by multiple target BER enzymes (i.e., uracil DNA glycosylase, thymine DNA glycosylase, and APE1), resulting in a unified fluorescent signal that significantly improves the detection specificity and sensitivity to BER enzymes. Additionally, we demonstrate that the GATED has exceptional biostability within complex biological systems, where it was successfully employed to monitor BER activity in cancer cells with high specificity and enabled cell-based high-throughput screening for BER inhibitors. The GATED provides a much-needed tool for the real-time monitoring of BER activity and the screening of BER inhibitors in cancer cells, potentially advancing both the investigation and clinical application of BER biology.

STAVER: a standardized benchmark dataset-based algorithm for effective variation reduction in large-scale DIA-MS data.

Mass spectrometry (MS)-based proteomics has become instrumental in comprehensively investigating complex biological systems. Data-independent acquisition (DIA)-MS, utilizing hybrid spectral library search strategies, allows for the simultaneous quantification of thousands of proteins, showing promise in enhancing protein identification and quantification precision. However, low-quality profiles can considerably undermine quantitative precision, resulting in inaccurate protein quantification. To tackle this challenge, we introduced STAVER, a novel algorithm that leverages standardized benchmark datasets to reduce non-biological variation in large-scale DIA-MS analyses. By eliminating unwanted noise in MS signals, STAVER significantly improved protein quantification precision, especially in hybrid spectral library searches. Moreover, we validated STAVER's robustness and applicability across multiple large-scale DIA datasets, demonstrating significantly enhanced precision and reproducibility of protein quantification. STAVER offers an innovative and effective approach for enhancing the quality of large-scale DIA proteomic data, facilitating cross-platform and cross-laboratory comparative analyses. This advancement significantly enhances the consistency and reliability of findings in clinical research. The complete package is available at https://github.com/Ran485/STAVER.

Digital oncology, the dawn of a new era turning the clock back on tumor mass

Evolutionary path of cancer therapeutics which started around eighty-two years ago, following serendipitous discovery of nitrogen mustard [1] has been guided by an accelerated pace of new discoveries, an ever-increasing depth of understanding of intracellular biological processes [2] and application of advances made in other fields of science [3]. Success in any field, especially in cancer medicine, is based on embracing evolution in understanding other scientific fields [4] and its application to the forward move in cancer therapeutics. As such, we have gone through discovery of a diverse group of therapeutics, targeting different sub compartments of growth and proliferation pathways [5] and taking advantage of their synergistic efficacy, in different combinations [6]. Dissection of immune regulatory pathways [7] and their interaction with cancer cell has led to the generation of modern immune therapeutic agents. Single-cell sequencing technology [8] has paved the way for the development of spatial omics [9], which has opened the way on deeper understanding of tumor evolutionary path [10]. This would give us the opportunity to design a new generation of cancer therapeutics [11] intercepting with the forward evolutionary path of tumor mass. Along this path, we face insurmountable barriers in developing meaningful and game changing treatment strategies by directing single changes in a hyper complex biological system [12], manifested by complexities of genome [13], epigenome [14], microRNA network [15], gene regulatory mechanisms [16], and protein-protein interactions [17]. Consequently, we need to search for a solution that enables us to freeze or slow down the forward evolutionary path of tumor mass [18], by making a single change in a game changing variable of cancer cell.

SMoRe GloS: An efficient and flexible framework for inferring global sensitivity of agent-based model parameters.

Agent-based models (ABMs) have become essential tools for simulating complex biological, ecological, and social systems where emergent behaviors arise from the interactions among individual agents. Quantifying uncertainty through global sensitivity analysis is crucial for assessing the robustness and reliability of ABM predictions. However, most global sensitivity methods demand substantial computational resources, making them impractical for highly complex models. Here, we introduce SMoRe GloS (Surrogate Modeling for Recapitulating Global Sensitivity), a novel, computationally efficient method for performing global sensitivity analysis of ABMs. By leveraging explicitly formulated surrogate models, SMoRe GloS allows for comprehensive parameter space exploration and uncertainty quantification without sacrificing accuracy. We demonstrate our method's flexibility by applying it to two biological ABMs: a simple 2D cell proliferation assay and a complex 3D vascular tumor growth model. Our results show that SMoRe GloS is compatible with simpler methods like the Morris one-at-a-time method, and more computationally intensive variance-based methods like eFAST. SMoRe GloS accurately recovered global sensitivity indices in each case while achieving substantial speedups, completing analyses in minutes. In contrast, direct implementation of eFAST amounted to several days of CPU time for the complex ABM. Remarkably, our method also estimates sensitivities for ABM parameters representing processes not explicitly included in the surrogate model, further enhancing its utility. By making global sensitivity analysis feasible for computationally expensive models, SMoRe GloS opens up new opportunities for uncertainty quantification in complex systems, allowing for more in depth exploration of model behavior, thereby increasing confidence in model predictions.

Classifying cell cycle states and a quiescent-like G0 state using single-cell transcriptomics.

Single-cell transcriptomics has unveiled a vast landscape of cellular heterogeneity in which the cell cycle is a significant component. We trained a high-resolution cell cycle classifier (ccAFv2) using single cell RNA-seq (scRNA-seq) characterized human neural stem cells. The ccAFv2 classifies six cell cycle states (G1, Late G1, S, S/G2, G2/M, and M/Early G1) and a quiescent-like G0 state (qG0), and it incorporates a tunable parameter to filter out less certain classifications. The ccAFv2 classifier performed better than or equivalent to other state-of-the-art methods even while classifying more cell cycle states, including G0. We demonstrate that the ccAFv2 classifier is generalizable across cell types and all three germ layers by applying it to developing fetal cells. We showcased the versatility of ccAFv2 by successfully applying it to classify cells, nuclei, and spatial transcriptomics data in humans and mice, using various normalization methods and gene identifiers. We provide methods to regress the cell cycle expression patterns out of single cell or nuclei data to uncover underlying biological signals. The classifier can be used either as an R package integrated with Seurat or a PyPI package integrated with scanpy. We proved that ccAFv2 has enhanced accuracy, flexibility, and adaptability across various experimental conditions, establishing ccAFv2 as a powerful tool for dissecting complex biological systems, unraveling cellular heterogeneity, and deciphering the molecular mechanisms by which proliferation and quiescence affect cellular processes.

DMF-ChIP-seq for Highly Sensitive and Integrated Epigenomic Profiling of Low-Input Cells.

Mapping genome-wide DNA-protein interactions (DPIs) provides insights into the epigenetic landscape of complex biological systems and elucidates the mechanisms of epigenetic regulation in biological progress. However, current technologies in DPI profiling still suffer from high cell demands, low detection sensitivity, and large reagent consumption. To address these problems, we developed DMF-ChIP-seq that builds on digital microfluidic (DMF) technology to profile genome-wide DPIs in a highly efficient, cost-effective, and user-friendly way. The entire workflow including cell pretreatment, antibody recognition, pA-Tn5 tagmentation, fragment enrichment, and PCR amplification is programmatically manipulated on a single chip. Leveraging closed submicroliter reaction volumes and a superhydrophobic interface, DMF-ChIP-seq presented higher sensitivity in peak enrichment than other current methods, with high accuracy (Pearson Correlation Coefficient (PCC) > 0.86) and high repeatability (PCC > 0.92). Furthermore, DMF-ChIP-seq was capable of processing the samples with as few as 8 cells while maintaining a high signal-to-noise ratio. By applying DMF-ChIP-seq, H3K27ac histone modification of early embryonic cells during differentiation was profiled for the investigation of epigenomic landscape dynamics. With the benefits of high efficiency and sensitivity in DPI analysis, the system provides great promise in studying epigenetic regulation during various biological processes.