Leveraging the capacity to precisely manipulate and analyze individual cells, single-cell technology has rapidly become an indispensable tool in the advancement of cell-based drug delivery systems and innovative cell therapies. This technology offers powerful means to address cellular heterogeneity and significantly enhance therapeutic efficacy. Recent breakthroughs in techniques such as single-cell electroporation, mechanical perforation, and encapsulation, particularly when integrated with microfluidics and bioelectronics, have led to remarkable improvements in drug delivery efficiency, reductions in cytotoxicity, and more precise targeting of therapeutic effects. Moreover, single-cell analyses, including advanced sequencing and high-resolution sensing, offer profound insights into complex disease mechanisms, the development of drug resistance, and the intricate processes of stem cell differentiation. This review summarizes the most significant applications of these single-cell technologies, highlighting their impact on the landscape of modern biomedicine. Furthermore, it provides a forward-looking perspective on future research directions aimed at further optimizing drug delivery strategies and enhancing therapeutic outcomes in the treatment of various diseases.

The applications of single-cell and spatial technologies in recent times have revolutionized the present understanding of cellular states and the cellular heterogeneity inherent in complex biological systems. These advancements offer unprecedented resolution in the examination of the functional genomics of individual cells and their spatial context within tissues. In this review, we have comprehensively discussed the historical development and recent progress in the field of single-cell and spatial genomics. We have reviewed the breakthroughs in single-cell multi-omics technologies, spatial genomics methods, and the computational strategies employed toward the analyses of single-cell atlas data. Furthermore, we have highlighted the advances made in constructing cellular atlases and their clinical applications, particularly in the context of disease. Finally, we have discussed the emerging trends, challenges, and opportunities in this rapidly evolving field.

Recent advancements in single-cell (sc) resolution analyses, particularly in sc transcriptomics and sc proteomics, have revolutionized our ability to probe and understand cellular heterogeneity. The study of metabolism through small molecules, metabolomics, provides an additional level of information otherwise unattainable by transcriptomics or proteomics by shedding light on the metabolic pathways that translate gene expression into functional outcomes. Metabolic heterogeneity, critical in health and disease, impacts developmental outcomes, disease progression, and treatment responses. However, dedicated approaches probing the sc metabolome have not reached the maturity of other sc omics technologies. Over the past decade, innovations in sc metabolomics have addressed some of the practical limitations, including cell isolation, signal sensitivity, and throughput. To fully exploit their potential in biological research, however, remaining challenges must be thoroughly addressed. Additionally, integrating sc metabolomics with orthogonal sc techniques will be required to validate relevant results and gain systems-level understanding. This perspective offers a broad-stroke overview of recent mass spectrometry (MS)-based sc metabolomics advancements, focusing on ongoing challenges from a biologist's viewpoint, aimed at addressing pertinent and innovative biological questions. Additionally, we emphasize the use of orthogonal approaches and showcase biological systems that these sophisticated methodologies are apt to explore.

Breast cancer is a leading cause of morbidity and mortality in women, and its progression is closely linked to the tumor microenvironment (TME). Cancer-associated fibroblasts (CAFs), key components of the TME, play a crucial role in promoting tumor growth by driving cancer cell proliferation, invasion, extracellular matrix (ECM) remodeling, inflammation, chemoresistance, and immunosuppression. CAFs exhibit considerable heterogeneity and are classified into subgroups based on different combinations of biomarkers. Single-cell RNA sequencing (scRNA-seq) enables high-throughput and high-resolution analysis of individual cells. Relying on this technology, it is possible to cluster complex CAFs according to different biomarkers to analyze the specific phenotypes and functions of different subpopulations. This review explores CAF clusters in breast cancer and their associated biomarkers, highlighting their roles in disease progression and potential for targeted therapies.

Following the recent emergence of RNA as a therapeutic tool, and coupled with an explosion in the development of new RNA technologies, it is rapidly becoming clear that the 21st century is the era of RNA. Neuroscience as a discipline has a long history of embracing new technology to advance the understanding of brain function, particularly in the context of learning and memory. In this short review, we highlight four broad categories of emerging RNA technologies, namely: imaging, isolation, identification and manipulation, and discuss their potential to advance the fundamental understanding of how RNA impacts experience-dependent plasticity, learning, and memory.

A lab-in-a-fiber component was fabricated using an optical fiber and a fiber capillary. It was used in a test suspension of fluorescently labeled and unlabeled cells and enabled detection of the labeled cells. Subsequently the labeled cells were selectively collected via suction into the capillary. A novel sampling technique reduced photobleaching of the labeled cells, extending the measurement time. The collected cells remained viable for downstream analysis. This platform's low fabrication cost, simplicity, compatibility with standard laboratory equipment, and capacity for fully automated cell capture highlights its potential for future applications in minimally invasive sample collection and point-of-care diagnostics. We demonstrate this LiF device to showcase the capability of optical fiber technology in creating low-cost, low-complexity cancer diagnostic devices. Furthermore, the LiF device holds promise for in vivo diagnostics, facilitating cell isolation and analysis.

Vascular endothelial cells (VECs) play a crucial role in the development and maintenance of vascular biology specific to the tissue types. Molecular signature-based labeling and imaging of VECs help researchers understand potential mechanisms linking VECs to disease pathology, serving as valuable biomarkers in clinical settings and trials. Labeling techniques involve selectively tagging or marking VECs for visualization. Immunolabeled employs antibodies that specifically bind to VECs markers, while fluorescent tracers or dyes can directly label VECs for imaging. Some techniques use specific carbohydrate residues on cell surface, while others employ endothelial-specific promoters to express fluorescent proteins. Additionally, VEC can be labeled with contrast agents, radiolabeled tracers, and nanoparticles. The choice of labeling technique depends on study context, including whether it involves animal models, in vitro cell cultures, or clinical applications. Herein, we discussed the various labeling methods utilized to label VECs and the techniques to visualize them.

This article reviews the impact of single-cell sequencing (SCS) on cancer biology research. SCS has revolutionized our understanding of cancer and tumor heterogeneity, clonal evolution, and the complex interplay between cancer cells and tumor microenvironment. SCS provides high-resolution profiling of individual cells in genomic, transcriptomic, and epigenomic landscapes, facilitating the detection of rare mutations, the characterization of cellular diversity, and the integration of molecular data with phenotypic traits. The integration of SCS with multi-omics has provided a multidimensional view of cellular states and regulatory mechanisms in cancer, uncovering novel regulatory mechanisms and therapeutic targets. Advances in computational tools, artificial intelligence (AI), and machine learning have been crucial in interpreting the vast amounts of data generated, leading to the identification of new biomarkers and the development of predictive models for patient stratification. Furthermore, there have been emerging technologies such as spatial transcriptomics and in situ sequencing, which promise to further enhance our understanding of tumor microenvironment organization and cellular interactions. As SCS and its related technologies continue to advance, they are expected to drive significant advances in personalized cancer diagnostics, prognosis, and therapy, ultimately improving patient outcomes in the era of precision oncology.

Cancer is a major global health challenge, with approximately 19.3 million new cases and 10 million deaths estimated by 2020. Laboratory advancements in cancer detection have transformed diagnostic capabilities, particularly through the use of biomarkers that play crucial roles in risk assessment, therapy selection, and disease monitoring. Tumor histology, single-cell technology, flow cytometry, molecular imaging, liquid biopsy, immunoassays, and molecular diagnostics have emerged as pivotal tools for cancer detection. The integration of artificial intelligence, particularly deep learning and convolutional neural networks, has enhanced the diagnostic accuracy and data analysis capabilities. However, developing countries face significant challenges including financial constraints, inadequate healthcare infrastructure, and limited access to advanced diagnostic technologies. The impact of COVID-19 has further complicated cancer management in resource-limited settings. Future research should focus on precision medicine and early cancer diagnosis through sophisticated laboratory techniques to improve prognosis and health outcomes. This review examines the evolving landscape of cancer detection, focusing on laboratory research breakthroughs and limitations in developing countries, while providing recommendations for advancing tumor diagnostics in resource-constrained environments.

Single-cell sequencing-based techniques are revolutionizing all fields of biomedical sciences, including normal kidney development and how this is disturbed in the development of Wilms tumor. The many different techniques and the differences between them can obscure which technique is best used to answer which question. In this review we summarize the techniques currently available, discuss which have been used in kidney development or Wilms tumor context, and which techniques can or should be combined to maximize the increase in biological understanding we can get from them.

Current therapeutic drugs are inadequate for curing tumors, highlighting the need for novel tumor drugs. The advancement of single-cell RNA sequencing (scRNA-seq) technology offers new opportunities for tumor drug discovery. This technology allows us to explore tumor heterogeneity and developmental mechanisms at the single-cell level. In this review, we outline the application of scRNA-seq in tumor drug discovery stages, including elucidating tumor mechanisms, identifying targets, screening drugs, and understanding drug action and resistance. We also discuss the challenges and future prospects of using scRNA-seq in drug development, providing a scientific foundation for advancing tumor therapies.

The challenges associated with traditional drug screening, such as high costs and long screening times, have led to an increase in the use of single-cell isolation technologies. Small sample volumes are required for high-throughput, cell-based assays to reduce assay costs and enable rapid sample processing. Using microfluidic chips, single-cell analysis can be conducted more effectively, requiring fewer reagents and maintaining biocompatibility. Due to the chip's ability to manipulate small volumes of fluid, high-throughput screening assays can be developed that are both miniaturized and automated. In the present study, we employ microfluidic chips for drug screening in chronic myeloid leukemia. This study aimed to establish a robust methodology integrating diverse assays, providing a holistic understanding of drug response.

Herein, we have used a chronic myeloid leukemia derived cell line (K562) for drug screening with an innovative microfluidic-based drug screening approach to investigate the efficacy of imatinib in K562 cells. Cell viability was assessed using MTT assay. Apoptosis was measured using Annexin/PI staining by flow cytometry.

Significant increased apoptosis was seen in K562 cells treated with imatinib in the microfluidic device compared to cells treated with imatinib in 24- and 96-well plates. Moreover, in the microfluidic chip, drug screening time was reduced from 48 hours to 24 hours.

Compared to traditional approaches, microfluidic-based drug screening efficiently evaluates the efficacy of imatinib in K562 cells. This approach is promising for drug discovery and treatment optimization, as it increases sensitivity and streamlines the screening process.

Single-cell protein analysis has emerged as a powerful tool for understanding cellular heterogeneity and deciphering the complex mechanisms governing cellular function and fate. This review provides a comprehensive examination of the latest methodologies, including sophisticated cell isolation techniques (Fluorescence-Activated Cell Sorting (FACS), Magnetic-Activated Cell Sorting (MACS), Laser Capture Microdissection (LCM), manual cell picking, and microfluidics) and advanced approaches for protein profiling and protein-protein interaction analysis. The unique strengths, limitations, and opportunities of each method are discussed, along with their contributions to unraveling gene regulatory networks, cellular states, and disease mechanisms. The importance of data analysis and computational methods in extracting meaningful biological insights from the complex data generated by these technologies is also highlighted. By discussing recent progress, technological innovations, and potential future directions, this review emphasizes the critical role of single-cell protein analysis in advancing life science research and its promising applications in precision medicine, biomarker discovery, and targeted therapeutics. Deciphering cellular complexity at the single-cell level holds immense potential for transforming our understanding of biological processes and ultimately improving human health.

Diabetic retinopathy is a major ocular complication of diabetes, characterized by progressive retinal microvascular damage and significant visual impairment in working-age adults. Traditional bulk RNA sequencing offers overall gene expression profiles but does not account for cellular heterogeneity. Single-cell RNA sequencing overcomes this limitation by providing transcriptomic data at the individual cell level and distinguishing novel cell subtypes, developmental trajectories, and intercellular communications. Researchers can use single-cell sequencing to draw retinal cell atlases and identify the transcriptomic features of retinal cells, enhancing our understanding of the pathogenesis and pathological changes in diabetic retinopathy. Additionally, single-cell sequencing is widely employed to analyze retinal organoids and single extracellular vesicles. Single-cell multi-omics sequencing integrates omics information, whereas stereo-sequencing analyzes gene expression and spatiotemporal data simultaneously. This review discusses the protocols of single-cell sequencing for obtaining single cells from retina and accurate sequencing data. It highlights the applications and advancements of single-cell sequencing in the study of normal retinas and the pathological changes associated with diabetic retinopathy. This underscores the potential of these technologies to deepen our understanding of the pathogenesis of diabetic retinopathy that may lead to the introduction of new therapeutic strategies.

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

To achieve a contactless and damage-less extraction of a single (or a few) human retinal pigment epithelial (RPE) cell(s) from a cell monolayer with acoustic droplet ejection.

An acoustic droplet ejector based on a self-focusing acoustic transducer (SFAT) is designed, microfabricated, and placed on a precision movable stage that aligns it to the targeted cell(s) in a Petri dish. The device delivers 20.1 MHz focused ultrasound (FUS) with a focal diameter of 100 μm, which ejects droplets capable of extracting and transferring only the targeted cell(s) from a monolayer.

The extraction and collection of 1-10 cells are successfully demonstrated. The number of ejected cells can be controlled by the FUS pulse width. As confirmed by fluorescence-based cell viability assays and re-culture experiments, the ejected cell(s) and remaining monolayer cells are intact without damage after cell ejection. Furthermore, real-time reverse transcription polymerase chain reaction (RT-PCR) tests for both housekeeping genes (GAPDH and -actin) and RPE-specific genes (MITF, PEDF, and PMEL17) show no significant difference between the acoustically ejected cells and those collected manually with a micropipette.

The proposed technology realizes a contactless, damage-free extraction of cells with high spatial resolution and precise control of the number of cells ejected, with a simple setup.

This powerful technology not only enables efficient, high-precision cell extraction for quality check applications but also opens new avenues for other advanced biotechnologies such as bioprinting.

As single-cell omics data sampling and acquisition methods have accumulated at an unprecedented rate, various data analysis pipelines have been developed for the inference of cell types, cell states and their distribution, state transitions, state trajectories, and state interactions. This presents a new opportunity in which single-cell omics data can be utilized to generate high-resolution, high-fidelity computational models. In this review, we discuss how single-cell omics data can be used to build computational models to simulate biological systems at various scales. We propose that single-cell data can be integrated with physiological information to generate organ-specific models, which can then be assembled to generate multi-organ systems pathophysiological models. Finally, we discuss how generic multi-organ models can be brought to the patient-specific level thus permitting their use in the clinical setting.

Liver cancer is a leading cause of death worldwide, representing a substantial public health challenge. The advent of single-cell RNA sequencing has significantly advanced our understanding of cellular dynamics from the onset of liver cancer to therapeutic intervention. This technology has unveiled profound insights into cancer heterogeneity and the tumor microenvironment (TME), enabling the identification of key molecular drivers and phenotypic landscapes of liver cancer at a single-cell resolution. This review highlights recent advancements in mapping functional cell subsets, phenotypic alterations, and the diversity of the TME. These insights are pivotal for advancing targeted therapies and developing prognostic tools. Moreover, this review covers the ongoing challenges and advances from tumor initiation to progression, offering a detailed perspective on advancing personalized treatment.

Raman-activated cell sorting isolates single cells in a nondestructive and label-free manner, but its throughput is limited by small spontaneous Raman scattering cross section. Coherent Raman scattering integrated with microfluidics enables high-throughput cell analysis, but faces challenges with small cells (<3 μm) and tissue sections. Here, we report stimulated Raman-activated cell ejection (S-RACE) that enables high-throughput single-cell sorting by integrating stimulated Raman imaging, in situ image decomposition, and laser-induced cell ejection. S-RACE allows ejection of live bacteria or fungi guided by their Raman signatures. Furthermore, S-RACE successfully sorted lipid-rich Rhodotorula glutinis cells from a cell mixture with a throughput of ~13 cells per second, and the sorting results were confirmed by downstream quantitative polymerase chain reaction. Beyond single cells, S-RACE shows high compatibility with tissue sections. Incorporating a closed-loop feedback control circuit further enables real-time SRS imaging-identification-ejection. In summary, S-RACE opens exciting opportunities for diverse single-cell sorting applications.

In recent years, single-cell RNA sequencing (scRNA-seq) has marked significant strides in livestock and poultry research, especially when integrated with multi-omics approaches. These advancements provide a nuanced view into complex regulatory networks and cellular dynamics. This review outlines the application of scRNA-seq in key species, including poultry, swine, and ruminants, with a focus on outcomes related to cellular heterogeneity, developmental biology, and reproductive mechanisms. We emphasize the synergistic power of combining scRNA-seq with epigenomic, proteomic, and spatial transcriptomic data, enhancing molecular breeding precision, optimizing health management strategies, and refining production traits in livestock and poultry. The integration of these technologies offers a multidimensional approach that not only broadens the scope of data analysis but also provides actionable insights for improving animal health and productivity.

Single-cell Sequencing technology (scSeq) has revolutionized our understanding of individual cells, uncovering unprecedented heterogeneity within tissues and cell populations, principality through single-cell RNA Sequencing (scRNA-Seq). This short review highlights the pivotal role of scRNA-Seq in elucidating genotype-phenotype relationships, particularly in biological systems. Based on published articles, our analysis involved manual curation and automated Scopus tools to illustrate recent advances in the application of scRNA-Seq. The results reveal that scRNA-Seq has been extensively utilized in various biological areas, including biochemistry, genetics, molecular biology, immunology, and microbiology, followed by health sciences covering studies related to the nervous system, immune system, human health, development, and diseases, with a particular focus on cancer research. However, the potential of scRNA-Seq extends beyond disease research, offering insights into non-model organisms' responses to environmental contaminants. By enabling the study of cellular reactions at a molecular level, scRNA-Seq provides a comprehensive understanding of intracellular heterogeneity that enhances our comprehension of physiological, biochemical, and pathological environmental impacts on non-model organisms exposed to pollution. This understanding has many practical benefits, as it can aid in regulation and conservation efforts that benefit the environment and the use of economically essential and ecologically relevant organisms.

Heterogeneity can impact biomarker identification. Thus, we investigated the somatic copy number alterations (SCNAs) of individual tumor cells in the vitreous humor of a retinoblastoma patient using single-cell whole-genome profiling and explored the genomic concordance among vitreous and aqueous humor, vitreous seeds, and tumor.

Aqueous humor (AH), vitreous humor (VH), and tumor biopsy were obtained from an enucleated globe with retinoblastoma and vitreous seeding. Micromanipulation was used to manually isolate 39 live single tumor cells from vitreous seeds harvested from the VH. The SCNA profiles of these individual cells were generated via whole-genome sequencing and analyzed alongside profiles from the tumor mass and cell-free DNA (cfDNA) from AH and VH.

Heatmap of VH single-cell SCNA profiles demonstrates heterogeneity among individual vitreous seeds with one clearly dominant subclone (23 of 37 cells). The SCNA profiles from the cells in this subclone demonstrate an average concordance of 98% with cfDNA profiles from acellular AH and VH and with the tumor profile.

Our findings reveal some heterogeneity among single-cell SCNA profiles in individual VH seeds. Despite this heterogeneity, the dominant vitreous subclone exhibits extremely (>98%) high concordance with the SCNA profile from tumor and AH, suggesting AH cfDNA is representative of the dominant genomic subclone. This may facilitate tumoral biomarker identification via the AH. This preliminary work supports the potential of applying single-cell technology to VH seeds in retinoblastoma as a platform to study tumor subclones, which may provide insight into the genomic complexity of disease.

Cardiovascular diseases (CVDs) impose a significant burden worldwide. Despite the elucidation of the etiology and underlying molecular mechanisms of CVDs by numerous studies and recent discovery of effective drugs, their morbidity, disability, and mortality are still high. Therefore, precise risk stratification and effective targeted therapies for CVDs are warranted. Recent improvements in single-cell RNA sequencing and spatial transcriptomics have improved our understanding of the mechanisms and cells involved in cardiovascular phylogeny and CVDs. Single-cell RNA sequencing can facilitate the study of the human heart at remarkably high resolution and cellular and molecular heterogeneity. However, this technique does not provide spatial information, which is essential for understanding homeostasis and disease. Spatial transcriptomics can elucidate intracellular interactions, transcription factor distribution, cell spatial localization, and molecular profiles of mRNA and identify cell populations causing the disease and their underlying mechanisms, including cell crosstalk. Herein, we introduce the main methods of RNA-seq and spatial transcriptomics analysis and highlight the latest advances in cardiovascular research. We conclude that single-cell RNA sequencing interprets disease progression in multiple dimensions, levels, perspectives, and dynamics by combining spatial and temporal characterization of the clinical phenome with multidisciplinary techniques such as spatial transcriptomics. This aligns with the dynamic evolution of CVDs (e.g., "angina-myocardial infarction-heart failure" in coronary artery disease). The study of pathways for disease onset and mechanisms (e.g., age, sex, comorbidities) in different patient subgroups should improve disease diagnosis and risk stratification. This can facilitate precise individualized treatment of CVDs.

Long-read single-cell transcriptomics (scRNA-Seq) is revolutionizing the way we profile heterogeneity in disease. Traditional short-read scRNA-Seq methods are limited in their ability to provide complete transcript coverage, resolve isoforms, and identify novel transcripts. The scRNA-Seq protocols developed for long-read sequencing platforms overcome these limitations by enabling the characterization of full-length transcripts. Long-read scRNA-Seq techniques initially suffered from comparatively poor accuracy compared to short read scRNA-Seq. However, with improvements in accuracy, accessibility, and cost efficiency, long-reads are gaining popularity in the field of scRNA-Seq. This review details the advances in long-read scRNA-Seq, with an emphasis on library preparation protocols and downstream bioinformatics analysis tools.

In recent years, the development of spatial transcriptomic technologies has enabled us to gain an in-depth understanding of the spatial heterogeneity of gene expression in biological tissues. However, a simple and efficient tool is required to analyze multiple spatial targets, such as mRNAs, miRNAs, or genetic mutations, at high resolution in formalin-fixed paraffin-embedded (FFPE) tissue sections. In this study, we developed hydrogel pathological sectioning coupled with the previously reported Sampling Junior instrument (HPSJ) to assess the spatial heterogeneity of multiple targets in FFPE sections at a scale of 180 μm. The HPSJ platform was used to demonstrate the spatial heterogeneity of 9 ferroptosis-related genes (TFRC, NCOA4, FTH1, ACSL4, LPCAT3, ALOX12, SLC7A11, GLS2, and GPX4) and 2 miRNAs (miR-185-5p and miR522) in FFPE tissue samples from patients with triple-negative breast cancer (TNBC). The results validated the significant heterogeneity of ferroptosis-related mRNAs and miRNAs. In addition, HPSJ confirmed the spatial heterogeneity of the L858R mutation in 7 operation-sourced and 4 needle-biopsy-sourced FFPE samples from patients with lung adenocarcinoma (LUAD). The successful detection of clinical FFPE samples indicates that HPSJ is a precise, high-throughput, cost-effective, and universal platform for analyzing spatial heterogeneity, which is beneficial for elucidating the mechanisms underlying drug resistance and guiding the prescription of mutant-targeted drugs in patients with tumors.

Metastasis frequently targets bones, where cancer cells from the primary tumour migrate to the bone marrow, initiating new tumour growth. Not only is bone the most common site for metastasis, but it also often marks the first site of metastatic recurrence. Despite causing over 90% of cancer-related deaths, effective treatments for bone metastasis are lacking, with current approaches mainly focusing on palliative care. Circulating tumour cells (CTCs) are pivotal in metastasis, originating from primary tumours and circulating in the bloodstream. They facilitate metastasis through molecular interactions with the bone marrow environment, involving direct cell-to-cell contacts and signalling molecules. CTCs infiltrate the bone marrow, transforming into disseminated tumour cells (DTCs). While some DTCs remain dormant, others become activated, leading to metastatic growth. The presence of DTCs in the bone marrow strongly correlates with future bone and visceral metastases. Research on CTCs in peripheral blood has shed light on their release mechanisms, yet investigations into bone marrow DTCs have been limited. Challenges include the invasiveness of bone marrow aspiration and the rarity of DTCs, complicating their isolation. However, advancements in single-cell analysis have facilitated insights into these elusive cells. This review will summarize recent advancements in understanding bone marrow DTCs using single-cell analysis techniques.

Laser microdissection technology is favored by biomedical researchers for its ability to rapidly and accurately isolate target cells and tissues. However, the precision cutting capabilities of existing laser microdissection systems are hindered by limitations in overall mechanical movement accuracy, resulting in suboptimal cutting quality. Additionally, the use of current laser microdissection systems for target acquisition may lead to tissue burns and reduced acquisition rates due to inherent flaws in the capture methods. To address these challenges and achieve precise and efficient separation and capture of cellular tissues, we integrated a digital micromirror device (DMD) into the existing system optics to modulate spatial light. This allows the system to not only implement the traditional point scanning cutting method but also utilize the projection cutting method.We have successfully cut various patterns on commonly used laser microdissection materials such as PET films and mouse tissues. Under projection cutting mode, we were able to achieve precise cutting of special shapes with a diameter of 7.5 micrometers in a single pass, which improved cutting precision and efficiency. Furthermore, we employed a negative pressure adsorption method to efficiently collect target substances. This approach not only resulted in a single-pass capture rate exceeding 90% for targets of different sizes but also enabled simultaneous capture of multiple targets, overcoming the limitations of traditional single-target capture and enhancing target capture efficiency, and avoiding potential tissue damage from lasers.In summary, the integration of the digital micromirror device into laser microdissection systems significantly enhances cutting precision and efficiency, overcoming limitations of traditional systems. This advancement demonstrates the accuracy and effectiveness of laser microdissection systems in isolating and capturing biological tissues, highlighting their potential in medical applications.

With the elucidation of community structures and assembly mechanisms in various fermented foods, core communities that significantly influence or guide fermentation have been pinpointed and used for exogenous restructuring into synthetic microbial communities (SynComs). These SynComs simulate ecological systems or function as adjuncts or substitutes in starters, and their efficacy has been widely verified. However, screening and assembly are still the main limiting factors for implementing theoretic SynComs, as desired strains cannot be effectively obtained and integrated. To expand strain screening methods suitable for SynComs in food fermentation, this review summarizes the recent research trends in using SynComs to study community evolution or interaction and improve the quality of food fermentation, as well as the specific process of constructing synthetic communities. The potential for novel screening modalities based on genes, enzymes and metabolites in food microbial screening is discussed, along with the emphasis on strategies to optimize assembly for facilitating the development of synthetic communities.

Mounting evidence underscores the significance of cellular diversity within the endocrine system and the intricate interplay between different cell types and tissues, essential for preserving physiological balance and influencing disease trajectories. The pituitary gland, a central player in the endocrine orchestra, exemplifies this complexity with its assortment of hormone-secreting and nonsecreting cells.

The pituitary gland houses several types of cells responsible for hormone production, alongside nonsecretory cells like fibroblasts and endothelial cells, each playing a crucial role in the gland's function and regulatory mechanisms. Despite the acknowledged importance of these cellular interactions, the detailed mechanisms by which they contribute to pituitary gland physiology and pathology remain largely uncharted. The last decade has seen the emergence of groundbreaking technologies such as single-cell RNA sequencing, offering unprecedented insights into cellular heterogeneity and interactions. However, the application of this advanced tool in exploring the pituitary gland's complexities has been scant. This review provides an overview of this methodology, highlighting its strengths and limitations, and discusses future possibilities for employing it to deepen our understanding of the pituitary gland and its dysfunction in disease states.

Single-cell RNA sequencing technology offers an unprecedented means to study the heterogeneity and interactions of pituitary cells, though its application has been limited thus far. Further utilization of this tool will help uncover the complex physiological and pathological mechanisms of the pituitary, advancing research and treatment of pituitary diseases.

The recent push toward understanding an individual cell's behavior and identifying cellular heterogeneity has created an unmet need for technologies that can probe live cells at the single-cell level. Cells within a population are known to exhibit heterogeneous responses to environmental cues. These differences can lead to varied cellular states, behavior, and responses to therapeutics. Techniques are needed that are not only capable of processing and analyzing cellular populations at the single cell level, but also have the ability to isolate specific cell populations from a complex sample at high throughputs. The new CellMag-Coalesce-Attract-Resegment Wash (CellMag-CARWash) system combines positive magnetic selection with droplet microfluidic devices to isolate cells of interest from a mixture with >93% purity and incorporate treatments within individual droplets to observe single cell biological responses. This workflow is shown to be capable of probing the single cell extracellular vesicle (EV) secretion of MCF7 GFP cells. This article reports the first measurement of β-Estradiol's effect on EV secretion from MCF7 cells at the single cell level. Single cell processing revealed that MCF7 GFP cells possess a heterogeneous response to β-Estradiol stimulation with a 1.8-fold increase relative to the control.

Single-cell RNA-seq (scRNA-seq) is a revolutionary technology that allows for the genomic investigation of individual cells in a population, allowing for the discovery of unusual cells associated with cancer and metastasis. ScRNA-seq has been used to discover different types of cancers with poor prognosis and medication resistance such as lung cancer, breast cancer, ovarian cancer, and gastric cancer. Besides, scRNA-seq is a promising method that helps us comprehend the biological features and dynamics of cell development, as well as other disorders. This review gives a concise summary of current scRNA-seq technology. We also explain the main technological steps involved in implementing the technology. We highlight the present applications of scRNA-seq in cancer research, including tumor heterogeneity analysis in lung cancer, breast cancer, and ovarian cancer. In addition, this review elucidates potential applications of scRNA-seq in lineage tracing, personalized medicine, illness prediction, and disease diagnosis, which reveals that scRNA-seq facilitates these events by producing genetic variations on the single-cell level.

Milk is a good source of nutrition but is also a source of allergenic proteins such as α-lactalbumin, β-lactoglobulin (BLG), casein, and immunoglobulins. The Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/Cas technology has the potential to edit any gene, including milk allergens. Previously, CRISPR/Cas has been successfully employed in dairy cows and goats, but buffaloes remain unexplored for any milk trait. In this study, we utilized the CRISPR/Cas9 system to edit the major milk allergen BLG gene in buffaloes. First, the editing efficiency of designed sgRNAs was tested in fibroblast cells using the T7E assay and Sanger sequencing. The most effective sgRNA was selected to generate clonal lines of BLG-edited cells. Analysis of 15 single-cell clones, through TA cloning and Sanger sequencing, revealed that 7 clones exhibited bi-allelic (-/-) heterozygous, bi-allelic (-/-) homozygous, and mono-allelic (-/+) disruptions in BLG. Bioinformatics prediction analysis confirmed that non-multiple-of-3 edited nucleotide cell clones have frame shifts and early truncation of BLG protein, while multiple-of-3 edited nucleotides resulted in slightly disoriented protein structures. Somatic cell nuclear transfer (SCNT) method was used to produce blastocyst-stage embryos that have similar developmental rates and quality with wild-type embryos. This study demonstrated the successful bi-allelic editing (-/-) of BLG in buffalo cells through CRISPR/Cas, followed by the production of BLG-edited blastocyst stage embryos using SCNT. With CRISPR and SCNT methods described herein, our long-term goal is to generate gene-edited buffaloes with BLG-free milk.

The mammalian central nervous system consists of a large number of cells, which contain not only different types of neurons, but also a large number of glial cells, such as astrocytes, oligodendrocytes, and microglia. These cells are capable of performing highly refined electrophysiological activities and providing the brain with functions such as nutritional support, information transmission and pathogen defense. The diversity of cell types and individual differences between cells have brought inspiration to the study of the mechanism of central nervous system diseases. In order to explore the role of different cells, a new technology, single-cell sequencing technology has emerged to perform specific analysis of high-throughput cell populations, and has been continuously developed. Single-cell sequencing technology can accurately analyze single-cell expression in mixed-cell populations and collect cells from different spatial locations, time stages and types. By using single-cell sequencing technology to compare gene expression profiles of normal and diseased cells, it is possible to discover cell subsets associated with specific diseases and their associated genes. Therefore, scientists can understand the development process, related functions and disease state of the nervous system from an unprecedented depth. In conclusion, single-cell sequencing technology provides a powerful technology for the discovery of novel therapeutic targets for central nervous system diseases.

Invasive Aspergillosis is a high-risk illness with a high death rate in immunocompromised people due to a lack of early detection and timely treatment. Based on immunology study, we achieved an efficient production of anti-galactomannan antibody by Chinese hamster ovary (CHO) cells and applied it to time-resolved fluoroimmunoassay for Aspergillus galactomannan detection. We first introduced dual promoter expression vector into CHO host cells, and then applied a two-step screening strategy to screen the stable cell line by methionine sulfoximine pressurization. After amplification and fermentation, antibody yield reached 4500 mg/L. Then we conjugated the antibodies with fluorescent microspheres to establish a double antibody sandwich time-resolved fluoroimmunoassay, which was compared with the commercial Platelia™ Aspergillus Ag by clinical serum samples. The preformed assay could obtain the results in less than 25 min, with a limit of detection for galactomannan of approximately 1 ng/mL. Clinical results of the two methods showed that the overall percent agreement was 97.7% (95% CI: 96.6%-98.4%) and Cohen's kappa coefficient was 0.94. Overall, the assay is highly consistent with commercial detection, providing a more sensitive and effective method for the rapid diagnosis of invasive aspergillosis.

Autoimmune diseases (AIDs) are complex in etiology and diverse in classification but clinically show similar symptoms such as joint pain and skin problems. As a result, the diagnosis is challenging, and usually, only broad treatments can be available. Consequently, the clinical responses in patients with different types of AIDs are unsatisfactory. Therefore, it is necessary to conduct more research to figure out the pathogenesis and therapeutic targets of AIDs. This requires research technologies with strong extraction and prediction capabilities. Single-cell sequencing technology analyses the genomic, epigenomic, or transcriptomic information at the single-cell level. It can define different cell types and states in greater detail, further revealing the molecular mechanisms that drive disease progression. These advantages enable cell biology research to achieve an unprecedented resolution and scale, bringing a whole new vision to life science research. In recent years, single-cell technology especially single-cell RNA sequencing (scRNA-seq) has been widely used in various disease research. In this paper, we present the innovations and applications of single-cell sequencing in the medical field and focus on the application contributing to the differential diagnosis and precise treatment of AIDs. Despite some limitations, single-cell sequencing has a wide range of applications in AIDs. We finally present a prospect for the development of single-cell sequencing. These ideas may provide some inspiration for subsequent research.

Cell mechanics is gaining attraction in drug screening, but the applicable methods have not yet become part of the standardized norm. This review presents the current state of the art for atomic force microscopy, which is the most widely available method. The field is first motivated as a new way of tracking pharmaceutical effects, followed by a basic introduction targeted at pharmacists on how to measure cellular stiffness. The review then moves on to the current state of the knowledge in terms of experimental results and supplementary methods such as fluorescence microscopy that can give relevant additional information. Finally, rheological approaches as well as the theoretical interpretations are presented before ending on additional methods and outlooks.

Although several technologies have been developed to isolate cells of interest from a heterogenous sample, clogging and impaired cell viability limit such isolation. We have developed the Enrich TROVO system as a novel, nonfluidic technology to sort live cells. The TROVO system combines imaging-based cell selection and photo-crosslinking of (gelatin methacrylate) gelMA-hydrogel to capture cells. After capture, cells are released by enzymatic digestion of the hydrogel and then retrieved for downstream analysis or further cell culturing. The system can capture cells with a recovery rate of 48% while maintaining 90% viability. Moreover, TROVO can enrich rare cells 506-fold with 93% efficiency using single step isolation from a 1:104 cell mixture, and can also capture one target cell from 1 million cells, reaching an enrichment ratio of 9128. In addition, 100% purity and 49% recovery rate can be achieved by a following negative isolation process. Compared to existing technologies, the TROVO system is clog-resistant, highly biocompatible, and can process a wide range of sample sizes.

Single-cell analysis, a rapidly evolving field, encounters significant challenges in detecting individual cells within complex plant tissues, particularly oil cells (OCs). The intricate process of single-cell isolation, coupled with the inherent chemical volatility of oil cells, necessitates a comprehensive methodology.

This study presents a method for obtaining intact OC from Asari Radix et Rhizoma (ARR), a traditional herbal medicine. The developed approach facilitates both qualitative and quantitative analysis of diverse OCs. To determine the most reliable approach, four practical methods-laser capture microdissection, micromanipulation capturing, micromanipulation piping, and cell picking-were systematically compared and evaluated, unequivocally establishing cell picking as the most effective method for OC isolation and chemical analysis. Microscopic observations showed that OCs predominantly distribute in the cortex of adventitious and fibrous roots, as well as the pith and cortex of the rhizome, with distinct morphologies-oblong in roots and circular in rhizomes. Sixty-three volatile constituents were identified in OCs, with eighteen compounds exhibiting significant differences. Safrole, methyleugenol, and asaricin emerged as the most abundant constituents in OCs. Notably, cis-4-thujanol and tetramethylpyrazine were exclusive to rhizome OCs, while isoeugenol methyl ether was specific to fibrous root OCs based on the detections. ARR roots and rhizomes displayed marked disparities in OC distribution, morphology, and constituents.

The study highlights the efficacy of cell picking coupled with HS-SPME-GC-MS as a flexible, reliable, and sensitive method for OC isolation and chemical analysis, providing a robust methodology for future endeavors in single-cell analyses.

Uveal cancer (UM) offers a complex molecular landscape characterized by substantial heterogeneity, both on the genetic and epigenetic levels. This heterogeneity plays a critical position in shaping the behavior and response to therapy for this uncommon ocular malignancy. Targeted treatments with gene-specific therapeutic molecules may prove useful in overcoming radiation resistance, however, the diverse molecular makeups of UM call for a patient-specific approach in therapy procedures. We need to understand the intricate molecular landscape of UM to develop targeted treatments customized to each patient's specific genetic mutations. One of the promising approaches is using liquid biopsies, such as circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA), for detecting and monitoring the disease at the early stages. These non-invasive methods can help us identify the most effective treatment strategies for each patient. Single-cellular is a brand-new analysis platform that gives treasured insights into diagnosis, prognosis, and remedy. The incorporation of this data with known clinical and genomics information will give a better understanding of the complicated molecular mechanisms that UM diseases exploit. In this review, we focused on the heterogeneity and molecular panorama of UM, and to achieve this goal, the authors conducted an exhaustive literature evaluation spanning 1998 to 2023, using keywords like "uveal melanoma, "heterogeneity". "Targeted therapies"," "CTCs," and "single-cellular analysis".

In forensics investigations, it is common to encounter biological mixtures consisting of homogeneous or heterogeneous components from multiple individuals and with different genetic contributions. One promising mixture deconvolution strategy is the DEPArray™ technology, which enables the separation of cell populations before genetic analysis. While technological advances are fundamental, their reliable validation is crucial for successful implementation and use for casework. Thus, this study aimed to 1) systematically validate the DEPArray™ system concerning specificity, sensitivity, repeatability, and contamination occurrences for blood, epithelial, and sperm cells, and 2) evaluate its potential for single-cell analysis in the field of forensic science. Our findings confirmed the effective identification of different cell types and the correct assignment of successfully genotyped single cells to their respective donor(s). Using the NGM Detect™ Amplification Kit, the average profile completeness for diploid cells was approximately 80%, with ∼ 290 RFUs. In contrast, haploid sperm analysis yielded an average completeness of 51% referring to the haploid reference profile, accompanied by mean peak heights of ∼ 176 RFUs. Although certain alleles of heterozygous loci in diploid cells showed strong imbalances, the overall peak balances yielded acceptable values above ≥ 60% with a mean value of 72% ± 0.21, a median of 77%, but with a maximum imbalance of 9% between heterozygous peaks. Locus dropouts were considered stochastic events, exhibiting variations among donors and cell types, with a notable failure incidence observed for TH01. Within the wet-lab experimentation with >500 single cells for the validation, profiling was performed using the consensus approach, where profiles were selected randomly from all data to better mirror real casework results. Nevertheless, complete profiles could be achieved with as few as three diploid cells, while the average success rate increased to 100% when using profiles of 6-10 cells. For sperms, however, a consensus profile with completeness >90% of the autosomal diploid genotype could be attained using ≥15 cells. In addition, the robustness of the consensus approach was evaluated in the absence of the respective reference profile without severe deterioration. Here, increased stutter peaks (≥ 15%) were found as the main artifact in single-cell profiles, while contamination and drop-ins were ascertained as rare events. Lastly, the technique's potential and limitations are discussed, and practical guidance is provided, particularly valuable for cold cases, multiple perpetrator rapes, and analyses of homogeneous mixed evidence.

Single-cell sequencing was developed as a high-throughput tool to elucidate unusual and transient cell states that are barely visible in the bulk. This technology reveals the evolutionary status of cells and differences between populations, helps to identify unique cell subtypes and states, reveals regulatory relationships between genes, targets and molecular mechanisms in disease processes, tumor heterogeneity, the state of the immune environment, etc. However, the high cost and technical limitations of single-cell sequencing initially prevented its widespread application, but with advances in research, numerous new single-cell sequencing techniques have been discovered, lowering the cost barrier. Many single-cell sequencing platforms and bioinformatics methods have recently become commercially available, allowing researchers to make fascinating observations. They are now increasingly being used in various industries. Several protocols have been discovered in this context and each technique has unique characteristics, capabilities and challenges. This review presents the latest advancements in single-cell transcriptomics technologies. This includes single-cell transcriptomics approaches, workflows and statistical approaches to data processing, as well as the potential advances, applications, opportunities and challenges of single-cell transcriptomics technology. You will also get an overview of the entry points for spatial transcriptomics and multi-omics.