Dissecting genomic diversity, one cell at a time

Exploring Microbial Dark Matter for the Discovery of Novel Natural Products: Characteristics, Abundance Challenges and Methods

The objective of this review is to investigate microbial dark matter (MDM) with a focus on its potential for discovering novel natural products (NPs). This first part will examine the characteristics and abundance of these previously unexplored microbial communities, as well as the challenges faced in identifying and harnessing their unique biochemical properties and novel methods in this field. MDMs are thought to hold great potential for the discovery of novel NPs, which could have significant applications in medicine, agriculture, and industry. In recent years, there has been a growing interest in exploring MDM to unlock its potential. In fact, developments in genome-sequencing technologies and sophisticated phylogenetic procedures and metagenomic techniques have contributed to drastically make important changes in our sights on the diversity of microbial life, including the very outline of the tree of life. This has led to the development of novel technologies and methodologies for studying these elusive microorganisms, such as single-cell genomics, metagenomics, and culturomics. These approaches enable researchers to isolate and analyze individual microbial cells, as well as entire communities, providing insights into their genetic and metabolic potential. By delving into the MDM, scientists hope to uncover new compounds and biotechnological advancements that could have far-reaching impacts on various fields.

Spatial landscapes of cancers: insights and opportunities

Solid tumours comprise many different cell types organized in spatially structured arrangements, with substantial intratumour and intertumour heterogeneity. Advances in spatial profiling technologies over the past decade hold promise to capture the complexity of these cellular architectures to build a holistic view of the intricate molecular mechanisms that shape the tumour ecosystem. Some of these mechanisms act at the cellular scale and are controlled by cell-autonomous programmes or communication between nearby cells, whereas other mechanisms result from coordinated efforts between large networks of cells and extracellular molecules organized into tissues and organs. In this Review we provide insights into the application of single-cell and spatial profiling tools, with a focus on spatially resolved transcriptomic tools developed to understand the cellular architecture of the tumour microenvironment and identify opportunities to use them to improve clinical management of cancers.

Single-cell transcriptome analysis reveals dynamic changes of the preclinical A549 cancer models, and the mechanism of dacomitinib

The in vitro A549 cells, and A549 xenografts in nude mouse, were two commonly used models for anti-cancer drug discovery. However, the biological and molecular characteristics of these two classic models, and also the dynamic transcriptome changes after dacomitinib exposure remains elusive. We performed single-cell RNA sequencing to define the transcriptome profile at single-cell resolution, and processed tumor samples for bulk RNA and protein analysis to validate the differently expressed genes. Transcriptome profiling revealed that the in vitro A549 cells are heterogeneous. The minimal subpopulation of the in vitro A549 cells, which were characterized by the signature of response to unfolded protein, became the overriding subpopulation of the xenografts. The EGFR non-activating A549 cells were resistant to dacomitinib in vitro, while A549 xenografts were comparatively sensitive as EGFR-activating HCC827 xenografts. Dacomitinib inhibited MAPK signaling pathway, and increased the immune response in the A549 xenografts. A phagocytosis checkpoint stanniocalcin-1 (STC1) was significantly inhibited in dacomitinib-treated xenografts. So here our study gives the first insight of the heterogeneity of the two classic models, and the translational potential of dacomitinib being used into a broader patient population rather than EGFR common activating mutation.

Single-Cell Isolation Microfluidic Chip Based on Thermal Bubble Micropump Technology

The isolation of single cells is essential for the development of single cell analysis methods, such as single-cell sequencing, monoclonal antibodies, and drug development. Traditional single-cell isolation techniques include flow cytometry (FACS), laser capture microdissection (LCM), micromanipulation, etc., but their operations are complex and have low throughput. Here, we present a microfluidic chip that can isolate individual cells from cell suspension and release them onto a well plate. It uses thermal bubble micropump technology to drive the fluid flow, and single-cell isolation is achieved by matching the flow resistance of the flow channel. Therefore, injection pumps and peristaltic pumps are not required for cell loading. Because of its small size, we can integrate hundreds of single-cell functional modules, which makes high-throughput single-cell isolation possible. For polystyrene beads, the capture rate of the single bead is close to 100%. Finally, the method has been applied to cells, and the capture rate of the single cell is also about 75%. This is a promising method for single-cell isolation.

Versatile, facile and low-cost single-cell isolation, culture and sequencing by optical tweezer-assisted pool-screening

Real-time image-based sorting of target cells in a precisely indexed manner is desirable for sequencing or cultivating individual human or microbial cells directly from clinical or environmental samples; however, the versatility of existing methods is limited as they are usually not broadly applicable to all cell sizes. Here, an optical tweezer-assisted pool-screening and single-cell isolation (OPSI) system is established for precise, indexed isolation of individual bacterial, yeast or human-cancer cells. A controllable static flow field that acts as a cell pool is achieved in a microfluidics chip, to enable precise and ready screening of cells of 1 to 40 μm in size by bright-field, fluorescence, or Raman imaging. The target cell is then captured by a 1064 nm optical tweezer and deposited as one-cell-harboring nanoliter microdroplets in a one-cell-one-tube manner. For bacterial, yeast and human cells, OPSI achieves a >99.7% target-cell sorting purity and a 10-fold elevated speed of 10-20 cells per min. Moreover, OPSI-based one-cell RNA-seq of human cancer cells yields high quality and reproducible single-cell transcriptome profiles. The versatility, facileness, flexibility, modularized design, and low cost of OPSI suggest its broad applications for image-based sorting of target cells.

Update on the Molecular Aspects and Methods Underlying the Complex Architecture of FSHD

Despite the knowledge of the main mechanisms involved in facioscapulohumeral muscular dystrophy (FSHD), the high heterogeneity and variable penetrance of the disease complicate the diagnosis, characterization and genotype–phenotype correlation of patients and families, raising the need for further research and data. Thus, the present review provides an update of the main molecular aspects underlying the complex architecture of FSHD, including the genetic factors (related to D4Z4 repeated units and FSHD-associated genes), epigenetic elements (D4Z4 methylation status, non-coding RNAs and high-order chromatin interactions) and gene expression profiles (FSHD transcriptome signatures both at bulk tissue and single-cell level). In addition, the review will also describe the methods currently available for investigating the above-mentioned features and how the resulting data may be combined with artificial-intelligence-based pipelines, with the purpose of developing a multifunctional tool tailored to enhancing the knowledge of disease pathophysiology and progression and fostering the research for novel treatment strategies, as well as clinically useful biomarkers. In conclusion, the present review highlights how FSHD should be regarded as a disease characterized by a molecular spectrum of genetic and epigenetic factors, whose alteration plays a differential role in DUX4 repression and, subsequently, contributes to determining the FSHD phenotype.

Estimating the number of available states for normal and tumor tissues in gene expression space

The topology of gene expression space for a set of 12 cancer types is studied by means of an entropy-like magnitude, which measures the volumes of the regions occupied by tumor and normal samples, i.e., the number of available states (genotypes) that can be classified as tumor-like or normal-like, respectively. Computations show that the number of available states is much greater for tumors than for normal tissues, suggesting the irreversibility of the progression to the tumor phase. The entropy is nearly constant for tumors, whereas it exhibits a higher variability in normal tissues, probably due to tissue differentiation. In addition, we show an interesting correlation between the fraction (tumor/normal) of available states and the overlap between the tumor and normal sample clouds, interpreted as a way of reducing the decay rate to the tumor phase in more ordered or structured tissues.

SelfE: Gene Selection via Self-Expression for Single-Cell Data

Single-cell RNA sequencing has been proved to be advantageous in discerning molecular heterogeneity in seemingly similar cells in a tissue. Due to the paucity of starting RNA, a large fraction of transcripts fail to amplify during the polymerase chain reaction cycle. This gets compounded by trivial biological noise such as variability in the cell cycle specific genes. As a result expression matrix obtained from a single-cell study is highly sparse with a large number of missing values. This hinders downstream analysis of single-cell expression data. It has been observed that feature engineering significantly improves the analysis outcomes. Feature extraction methods such as principal component analysis and zero-inflated factor analysis have been shown to be useful for subsequent steps of data analysis including clustering. However, too little or no visible efforts have been observed for developing feature selection techniques, which offer transparency for the analyst's consumption. We propose SelfE, a novel l_2,0 -minimization algorithm that determines an optimal subset of feature vectors that preserves sub-space structures as observed in the data. We compared SelfE with the commonly used feature selection methods for single-cell expression data analysis.

Single cell RNA sequencing techniques and applications in research of ovary development and related diseases

The ovary is a highly organized composite of germ cells and various types of somatic cells, whose communications dictate ovary development to generate functional oocytes. The differences between individual cells might have profound effects on ovary functions. Single cell RNA sequencing techniques are promising approaches to explore the cell type composition of organisms, the dynamics of transcriptomes, the regulatory network between genes and the signaling pathways between cell types at the single cell resolution. In this review, we provide an overview of the currently available single cell RNA sequencing techniques including Smart-seq2 and Drop-seq, as well as their applications in biological and clinical research to provide a better understanding on the molecular mechanisms underlying ovary development and associated diseases.

Microfluidics applications for high-throughput single cell sequencing

The inherent heterogeneity of individual cells in cell populations plays significant roles in disease development and progression, which is critical for disease diagnosis and treatment. Substantial evidences show that the majority of traditional gene profiling methods mask the difference of individual cells. Single cell sequencing can provide data to characterize the inherent heterogeneity of individual cells, and reveal complex and rare cell populations. Different microfluidic technologies have emerged for single cell researches and become the frontiers and hot topics over the past decade. In this review article, we introduce the processes of single cell sequencing, and review the principles of microfluidics for single cell analysis. Also, we discuss the common high-throughput single cell sequencing technologies along with their advantages and disadvantages. Lastly, microfluidics applications in single cell sequencing technology for the diagnosis of cancers and immune system diseases are briefly illustrated.

Prokaryotic and eukaryotic diversity in hydrothermal continental systems

The term extremophile was suggested more than 30 years ago and represents microorganisms that are capable of developing and living under extreme conditions, these conditions being particularly hostile to other types of microorganisms and to humankind. In terrestrial hydrothermal sites, like hot springs, "mud pools", solfataras, and geysers, the dominant extreme conditions are high temperature, low or high pH, and high levels of salinity. The diversity of microorganisms inhabiting these sites is determined by the conditions of the environment. Organisms belonging to the domains Archaea and Bacteria are more represented than the one belonging to Eukarya. Eukarya members tend to be less present because of their lower tolerance to higher temperatures, however, they perform important ecosystem processes when present. Both prokaryotes and eukaryotes have morphological and physical adaptations that allow them to colonize extreme environments. Microbial mats are complex associations of microorganisms that help the colonization of more extreme systems. In this review, a characterization of prokaryotic and eukaryotic organisms that populate terrestrial hydrothermal systems are made.

Strategies for Natural Products Discovery from Uncultured Microorganisms

Microorganisms are highly regarded as a prominent source of natural products that have significant importance in many fields such as medicine, farming, environmental safety, and material production. Due to this, only tiny amounts of microorganisms can be cultivated under standard laboratory conditions, and the bulk of microorganisms in the ecosystems are still unidentified, which restricts our knowledge of uncultured microbial metabolism. However, they could hypothetically provide a large collection of innovative natural products. Culture-independent metagenomics study has the ability to address core questions in the potential of NP production by cloning and analysis of microbial DNA derived directly from environmental samples. Latest advancements in next generation sequencing and genetic engineering tools for genome assembly have broadened the scope of metagenomics to offer perspectives into the life of uncultured microorganisms. In this review, we cover the methods of metagenomic library construction, and heterologous expression for the exploration and development of the environmental metabolome and focus on the function-based metagenomics, sequencing-based metagenomics, and single-cell metagenomics of uncultured microorganisms.

Gene expression rearrangements denoting changes in the biological state

In many situations, the gene expression signature is a unique marker of the biological state. We study the modification of the gene expression distribution function when the biological state of a system experiences a change. This change may be the result of a selective pressure, as in the Long Term Evolution Experiment with E. Coli populations, or the progression to Alzheimer disease in aged brains, or the progression from a normal tissue to the cancer state. The first two cases seem to belong to a class of transitions, where the initial and final states are relatively close to each other, and the distribution function for the differential expressions is short ranged, with a tail of only a few dozens of strongly varying genes. In the latter case, cancer, the initial and final states are far apart and separated by a low-fitness barrier. The distribution function shows a very heavy tail, with thousands of silenced and over-expressed genes. We characterize the biological states by means of their principal component representations, and the expression distribution functions by their maximal and minimal differential expression values and the exponents of the Pareto laws describing the tails.

Distribution and Antibiotic Resistance Profiles of Salmonella enterica in Rural Areas of North Carolina After Hurricane Florence in 2018

In this study, water samples were analyzed from a rural area of North Carolina after Hurricane Florence in 2018 and the distribution of the ttrC virulence gene of Salmonella enterica were investigated. We also examined the distribution of culturable S. enterica and determined their antibiotic resistance profiles. Antibiotic resistance genes (ARGs) in the classes of aminoglycoside, beta-lactam, and macrolide-lincosamide-streptogramin B (MLSB) were targeted in this study. The ttrC gene was detected in 23 out of 25 locations. There was a wider and higher range of the ttrC gene in flooded water versus unflooded water samples (0-2.12 × 105 copies/L vs. 0-4.86 × 104 copies/L). Culturable S. enterica was isolated from 10 of 25 sampling locations, which was less prevalent than the distribution of the ttrC gene. The antibiotic resistance profiles were not distinct among the S. enterica isolates. The aminoglycoside resistance gene aac(6')-Iy had the highest relative abundance (around 0.05 copies/16S rRNA gene copy in all isolates) among all ARGs. These findings suggested that the 2018 flooding event led to higher copy numbers of the ttrC genes of S. enterica in some flooded water bodies compared to those in unflooded water bodies. The high ARG level and similar ARG profiles were observed in all S. enterica isolates from both flooded and unflooded samples, suggesting that the antibiotic resistance was prevalent in S. enterica within this region, regardless of flooding.

HPV+ve/-ve oral-tongue cancer stem cells: A potential target for relapse-free therapy

The tongue squamous cell carcinoma (TSCC) is a highly prevalent head and neck cancer often associated with tobacco and/or alcohol abuse or high-risk human papillomavirus (HR-HPV) infection. HPV positive TSCCs present a unique mechanism of tumorigenesis as compared to tobacco and alcohol-induced TSCCs and show a better prognosis when treated. The poor prognosis and/or recurrence of TSCC is due to presence of a small subpopulation of tumor-initiating tongue cancer stem cells (TCSCs) that are intrinsically resistant to conventional chemoradio-therapies enabling cancer to relapse. Therefore, targeting TCSCs may provide efficient therapeutic strategy for relapse-free survival of TSCC patients. Indeed, the development of new TCSC targeting therapeutic approaches for the successful elimination of HPV+ve/-ve TCSCs could be achieved either by targeting the self-renewal pathways, epithelial mesenchymal transition, vascular niche, nanoparticles-based therapy, induction of differentiation, chemoradio-sensitization of TCSCs or TCSC-derived exosome-based drug delivery and inhibition of HPV oncogenes or by regulating epigenetic pathways. In this review, we have discussed all these potential approaches and highlighted several important signaling pathways/networks involved in the formation and maintenance of TCSCs, which are targetable as novel therapeutic targets to sensitize/eliminate TCSCs and to improve survival of TSCC patients.

Data Integration for Immunology

Over the last several years, next-generation sequencing and its recent push toward single-cell resolution have transformed the landscape of immunology research by revealing novel complexities about all components of the immune system. With the vast amounts of diverse data currently being generated, and with the methods of analyzing and combining diverse data improving as well, integrative systems approaches are becoming more powerful. Previous integrative approaches have combined multiple data types and revealed ways that the immune system, both as a whole and as individual parts, is affected by genetics, the microbiome, and other factors. In this review, we explore the data types that are available for studying immunology with an integrative systems approach, as well as the current strategies and challenges for conducting such analyses.

Image-Based Single Cell Sorting Automation in Droplet Microfluidics

The recent boom in single-cell omics has brought researchers one step closer to understanding the biological mechanisms associated with cell heterogeneity. Rare cells that have historically been obscured by bulk measurement techniques are being studied by single cell analysis and providing valuable insight into cell function. To support this progress, novel upstream capabilities are required for single cell preparation for analysis. Presented here is a droplet microfluidic, image-based single-cell sorting technique that is flexible and programmable. The automated system performs real-time dual-camera imaging (brightfield & fluorescent), processing, decision making and sorting verification. To demonstrate capabilities, the system was used to overcome the Poisson loading problem by sorting for droplets containing a single red blood cell with 85% purity. Furthermore, fluorescent imaging and machine learning was used to load single K562 cells amongst clusters based on their instantaneous size and circularity. The presented system aspires to replace manual cell handling techniques by translating expert knowledge into cell sorting automation via machine learning algorithms. This powerful technique finds application in the enrichment of single cells based on their micrographs for further downstream processing and analysis.

Differential detachment of intact and viable cells of different sizes using laser-induced microbubbles

Single cell isolation is a prerequisite for the analysis of rare or small cell subtypes. Here, we selectively detach single cells in a heterogeneous population comprised of different morphological subtypes whose sizes vary in body and extension. Such a cellular environment is first accommodated for by a photomechanical method in which pulsed laser irradiation produces microbubbles from a polymer substrate, thus pushing out and detaching cultured cells in an intact, viable, and spatially tailored way. While this has previously only bene used at a very low cell density with lack of quantitative characterization, we determine optimal detachment conditions for different cell sizes in terms of an optical fluence and the number of laser pulses. Importantly, our approach is employed to isolate cancer cells with inherent size variation and elucidate cellular heterogeneity in drug sensitivity: i.e., higher resistance for larger cell size. For cells detached by laser-induced microbubbles, morphology, proliferation, and viability are compared with those of conventional trypsin-treated cells detached without any spatial selectivity. These results support the suitability of our photomechanical method for biochemical screen and secondary analysis of cells with unusual responses.

Structure-preserving visualisation of high dimensional single-cell datasets

Single-cell technologies offer an unprecedented opportunity to effectively characterize cellular heterogeneity in health and disease. Nevertheless, visualisation and interpretation of these multi-dimensional datasets remains a challenge. We present a novel framework, ivis, for dimensionality reduction of single-cell expression data. ivis utilizes a siamese neural network architecture that is trained using a novel triplet loss function. Results on simulated and real datasets demonstrate that ivis preserves global data structures in a low-dimensional space, adds new data points to existing embeddings using a parametric mapping function, and scales linearly to hundreds of thousands of cells. ivis is made publicly available through Python and R interfaces on https://github.com/beringresearch/ivis .

Nanoscale tweezers for single-cell biopsies

Much of the functionality of multicellular systems arises from the spatial organization and dynamic behaviours within and between cells. Current single-cell genomic methods only provide a transcriptional 'snapshot' of individual cells. The real-time analysis and perturbation of living cells would generate a step change in single-cell analysis. Here we describe minimally invasive nanotweezers that can be spatially controlled to extract samples from living cells with single-molecule precision. They consist of two closely spaced electrodes with gaps as small as 10-20 nm, which can be used for the dielectrophoretic trapping of DNA and proteins. Aside from trapping single molecules, we also extract nucleic acids for gene expression analysis from living cells without affecting their viability. Finally, we report on the trapping and extraction of a single mitochondrion. This work bridges the gap between single-molecule/organelle manipulation and cell biology and can ultimately enable a better understanding of living cells.

Particle-Templated Emulsification for Microfluidics-Free Digital Biology

The compartmentalization of reactions in monodispersed droplets is valuable for applications across biology. However, the requirement of microfluidics to partition the sample into monodispersed droplets is a significant barrier that impedes implementation. Here, we introduce particle-templated emulsification, a method to encapsulate samples in monodispersed emulsions without microfluidics. By vortexing a mixture of hydrogel particles and sample solution, we encapsulate the sample in monodispersed emulsions that are useful for most droplet applications. We illustrate the method with ddPCR and single cell culture. The ability to encapsulate samples in monodispersed droplets without microfluidics should facilitate the implementation of compartmentalized reactions in biology.

Developing novel methods to image and visualize 3D genomes

To investigate three-dimensional (3D) genome organization in prokaryotic and eukaryotic cells, three main strategies are employed, namely nuclear proximity ligation-based methods, imaging tools (such as fluorescence in situ hybridization (FISH) and its derivatives), and computational/visualization methods. Proximity ligation-based methods are based on digestion and re-ligation of physically proximal cross-linked chromatin fragments accompanied by massively parallel DNA sequencing to measure the relative spatial proximity between genomic loci. Imaging tools enable direct visualization and quantification of spatial distances between genomic loci, and advanced implementation of (super-resolution) microscopy helps to significantly improve the resolution of images. Computational methods are used to map global 3D genome structures at various scales driven by experimental data, and visualization methods are used to visualize genome 3D structures in virtual 3D space-based on algorithms. In this review, we focus on the introduction of novel imaging and visualization methods to study 3D genomes. First, we introduce the progress made recently in 3D genome imaging in both fixed cell and live cells based on long-probe labeling, short-probe labeling, RNA FISH, and the CRISPR system. As the fluorescence-capturing capability of a particular microscope is very important for the sensitivity of bioimaging experiments, we also introduce two novel super-resolution microscopy methods, SDOM and low-power super-resolution STED, which have potential for time-lapse super-resolution live-cell imaging of chromatin. Finally, we review some software tools developed recently to visualize proximity ligation-based data. The imaging and visualization methods are complementary to each other, and all three strategies are not mutually exclusive. These methods provide powerful tools to explore the mechanisms of gene regulation and transcription in cell nuclei.

Single-Cell Transcriptional Analysis

Despite being a relatively recent technological development, single-cell transcriptional analysis through high-throughput sequencing has already been used in hundreds of fruitful studies to make exciting new biological discoveries that would otherwise be challenging or even impossible. Consequently, this has fueled a virtuous cycle of even greater interest in the field and compelled development of further improved technical methodologies and approaches. Thanks to the combined efforts of the research community, including the fields of biochemistry and molecular biology, technology and instrumentation, data science, computational biology, and bioinformatics, the single-cell RNA-sequencing field is advancing at a pace that is both astounding and unprecedented. In this review, we provide a broad introduction to this revolutionary technology by presenting the state-of-the-art in sample preparation methodologies, technology platforms, and computational analysis methods, while highlighting the key considerations for designing, executing, and interpreting a study using single-cell RNA sequencing.

Challenges and emerging directions in single-cell analysis

Single-cell analysis is a rapidly evolving approach to characterize genome-scale molecular information at the individual cell level. Development of single-cell technologies and computational methods has enabled systematic investigation of cellular heterogeneity in a wide range of tissues and cell populations, yielding fresh insights into the composition, dynamics, and regulatory mechanisms of cell states in development and disease. Despite substantial advances, significant challenges remain in the analysis, integration, and interpretation of single-cell omics data. Here, we discuss the state of the field and recent advances and look to future opportunities.

Electrochemical Genetic Profiling of Single Cancer Cells

Recent understandings in the development and spread of cancer have led to the realization of novel single cell analysis platforms focused on circulating tumor cells (CTCs). A simple, rapid, and inexpensive analytical platform capable of providing genetic information on these rare cells is highly desirable to support clinicians and researchers alike to either support the selection or adjustment of therapy or provide fundamental insights into cell function and cancer progression mechanisms. We report on the genetic profiling of single cancer cells, exploiting a combination of multiplex ligation-dependent probe amplification (MLPA) and electrochemical detection. Cells were isolated using laser capture and lysed, and the mRNA was extracted and transcribed into DNA. Seven markers were amplified by MLPA, which allows for the simultaneous amplification of multiple targets with a single primer pair, using MLPA probes containing unique barcode sequences. Capture probes complementary to each of these barcode sequences were immobilized on a printed circuit board (PCB) manufactured electrode array and exposed to single-stranded MLPA products and subsequently to a single stranded DNA reporter probe bearing a HRP molecule, followed by substrate addition and fast electrochemical pulse amperometric detection. We present a simple, rapid, flexible, and inexpensive approach for the simultaneous quantification of multiple breast cancer related mRNA markers, with single tumor cell sensitivity.

The Importance of Endophenotypes to Evaluate the Relationship between Genotype and External Phenotype

With the exception of a few Mendelian traits, almost all phenotypes (traits) in livestock science are quantitative or complex traits regulated by the expression of many genes. For most of the complex traits, differential expression of genes, rather than genomic variation in the gene coding sequences, is associated with the genotype of a trait. The expression profiles of the animal's transcriptome, proteome and metabolome represent endophenotypes that influence/regulate the externally-observed phenotype. These expression profiles are generated by interactions between the animal's genome and its environment that range from the cellular, up to the husbandry environment. Thus, understanding complex traits requires knowledge about not only genomic variation, but also environmental effects that affect genome expression. Gene products act together in physiological pathways and interaction networks (of pathways). Due to the lack of annotation of the functional genome and ontologies of genes, our knowledge about the various biological systems that contribute to the development of external phenotypes is sparse. Furthermore, interaction with the animals' microbiome, especially in the gut, greatly influences the external phenotype. We conclude that a detailed understanding of complex traits requires not only understanding of variation in the genome, but also its expression at all functional levels.

Spinning micropipette liquid emulsion generator for single cell whole genome amplification

Many on-chip approaches that use flow-focusing to pinch the continuous aqueous phase into droplets have become the most popular methods that provide monodisperse emulsion droplets. However, not every lab can easily adapt a microfluidic workflow into their familiar protocols. We develop an off-chip approach, spinning micro-pipette liquid emulsion (SiMPLE) generator, to produce highly stable monodisperse water-in-oil emulsions using a moving micropipette to disperse the aqueous phase in an oil-filled microcentrifuge tube. This method provides a simple way to produce picoliter-size droplets in situ with no dead volume during emulsification. With SiMPLE, single-cell emulsion whole genome amplification was performed to demonstrate that this novel method can seamlessly be integrated with experimental operations and supplies that most researchers are familiar with. The SiMPLE generator has effectively lowered the technical difficulties in applications relying on emulsion droplets.

Gene Expression in Single Cells Isolated from the CWR-R1 Prostate Cancer Cell Line and Human Prostate Tissue Based on the Side Population Phenotype

Defining biological signals at the single cell level can identify cancer initiating driver mutations. Techniques to isolate single cells such as microfluidics sorting and magnetic capturing systems have limitations such as: high cost, labor intense, and the requirement of a large number of cells. Therefore, the goal of our current study is to identify a cost and labor effective, reliable, and reproducible technique that allows single cell isolation for analysis to promote regular laboratory use, including standard reverse transcription PCR (RT-PCR).

In the current study, we utilized single prostate cells isolated from the CWR-R1 prostate cancer cell line and human prostate clinical specimens, based on the ATP binding cassette (ABC) transporter efflux of dye cycle violet (DCV), side population assay. Expression of four genes: ABCG2; Aldehyde dehydrogenase1A1 (ALDH1A1); androgen receptor (AR); and embryonic stem cell marker, Oct-4, were determined.

Results from the current study in the CWR-R1 cell line showed ABCG2 and ALDH1A1 gene expression in 67% of single side population cells and in 17% or 100% of non-side population cells respectively. Studies using single cells isolated from clinical specimens showed that the Oct-4 gene is detected in only 22% of single side population cells and in 78% of single non-side population cells. Whereas, AR gene expression is in 100% single side population and non-side population cells isolated from the same human prostate clinical specimen.

These studies show that performing RT-PCR on single cells isolated by FACS can be successfully conducted to determine gene expression in single cells from cell lines and enzymatically digested tissue. While these studies provide a simple yes/no expression readout, the more sensitive quantitative RT-PCR would be able to provide even more information if necessary.

Single Cell Isolation and Analysis

Individual cell heterogeneity within a population can be critical to its peculiar function and fate. Subpopulations studies with mixed mutants and wild types may not be as informative regarding which cell responds to which drugs or clinical treatments. Cell to cell differences in RNA transcripts and protein expression can be key to answering questions in cancer, neurobiology, stem cell biology, immunology, and developmental biology. Conventional cell-based assays mainly analyze the average responses from a population of cells, without regarding individual cell phenotypes. To better understand the variations from cell to cell, scientists need to use single cell analyses to provide more detailed information for therapeutic decision making in precision medicine. In this review, we focus on the recent developments in single cell isolation and analysis, which include technologies, analyses and main applications. Here, we summarize the historical background, limitations, applications, and potential of single cell isolation technologies.

Virtual microfluidics for digital quantification and single-cell sequencing

Interest in highly parallelized analysis of single molecules and single cells is growing rapidly. Here we develop hydrogel-based virtual microfluidics as a simple alternative to complex engineered microfluidic systems for the compartmentalization of nucleic acid amplification reactions. We applied digital multiple displacement amplification (dMDA) to purified DNA templates, cultured bacterial cells, and human microbiome samples in the virtual microfluidics system and demonstrated recovery and whole-genome sequencing of single-cell MDA products. Our results from control samples showed excellent coverage uniformity and markedly reduced chimerism compared with single-cell data obtained from conventional liquid MDA reactions. We also demonstrate the applicability of the hydrogel method for genomic studies of naturally occurring microbes in human microbiome samples. The virtual microfluidics approach is a simple and robust method that will enable many laboratories to perform single-cell genomic analyses.

Cryptosporidium as a testbed for single cell genome characterization of unicellular eukaryotes

Background

Infectious disease involving multiple genetically distinct populations of pathogens is frequently concurrent, but difficult to detect or describe with current routine methodology. Cryptosporidium sp. is a widespread gastrointestinal protozoan of global significance in both animals and humans.

It cannot be easily maintained in culture and infections of multiple strains have been reported.

To explore the potential use of single cell genomics methodology for revealing genome-level variation in clinical samples from Cryptosporidium-infected hosts, we sorted individual oocysts for subsequent genome amplification and full-genome sequencing.

Results

Cells were identified with fluorescent antibodies with an 80 % success rate for the entire single cell genomics workflow, demonstrating that the methodology can be applied directly to purified fecal samples. Ten amplified genomes from sorted single cells were selected for genome sequencing and compared both to the original population and a reference genome in order to evaluate the accuracy and performance of the method. Single cell genome coverage was on average 81 % even with a moderate sequencing effort and by combining the 10 single cell genomes, the full genome was accounted for. By a comparison to the original sample, biological variation could be distinguished and separated from noise introduced in the amplification.

Conclusions

As a proof of principle, we have demonstrated the power of applying single cell genomics to dissect infectious disease caused by closely related parasite species or subtypes. The workflow can easily be expanded and adapted to target other protozoans, and potential applications include mapping genome-encoded traits, virulence, pathogenicity, host specificity and resistance at the level of cells as truly meaningful biological units.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-2815-y) contains supplementary material, which is available to authorized users.

Microfluidic single-cell transcriptional analysis rationally identifies novel surface marker profiles to enhance cell-based therapies

Current progenitor cell therapies have only modest efficacy, which has limited their clinical adoption. This may be the result of a cellular heterogeneity that decreases the number of functional progenitors delivered to diseased tissue, and prevents correction of underlying pathologic cell population disruptions. Here, we develop a high-resolution method of identifying phenotypically distinct progenitor cell subpopulations via single-cell transcriptional analysis and advanced bioinformatics. When combined with high-throughput cell surface marker screening, this approach facilitates the rational selection of surface markers for prospective isolation of cell subpopulations with desired transcriptional profiles. We establish the usefulness of this platform in costly and highly morbid diabetic wounds by identifying a subpopulation of progenitor cells that is dysfunctional in the diabetic state, and normalizes diabetic wound healing rates following allogeneic application. We believe this work presents a logical framework for the development of targeted cell therapies that can be customized to any clinical application.

Unrecognized progenitor cell perturbations underlying a disease state may limit the efficacy of cell therapies. Here, the authors use high-throughput, single-cell transcriptional analysis to identify disease-specific cellular alterations and prospectively isolate restorative cell subpopulations.

Multiple Subsets of Brain Tumor Initiating Cells Coexist in Glioblastoma

Brain tumor-initiating cells (BTICs) are self-renewing multipotent cells critical for tumor maintenance and growth. Using single-cell microfluidic profiling, we identified multiple subpopulations of BTICs coexisting in human glioblastoma, characterized by distinct surface marker expression and single-cell molecular profiles relating to divergent bulk tissue molecular subtypes. These data suggest BTIC subpopulation heterogeneity as an underlying source of intra-tumoral bulk tissue molecular heterogeneity, and will support future studies into BTIC subpopulation-specific therapies. Stem Cells 2016;34:1702-1707.

High-Resolution Microfluidic Single-Cell Transcriptional Profiling Reveals Clinically Relevant Subtypes among Human Stem Cell Populations Commonly Utilized in Cell-Based Therapies

Stem cell therapies can promote neural repair and regeneration, yet controversy regarding optimal cell source and mechanism of action has slowed clinical translation, potentially due to undefined cellular heterogeneity. Single-cell resolution is needed to identify clinically relevant subpopulations with the highest therapeutic relevance. We combine single-cell microfluidic analysis with advanced computational modeling to study for the first time two common sources for cell-based therapies, human NSCs and MSCs. This methodology has the potential to logically inform cell source decisions for any clinical application.

Single-cell analysis tools for drug discovery and development

The genetic, functional or compositional heterogeneity of healthy and diseased tissues presents major challenges in drug discovery and development. Such heterogeneity hinders the design of accurate disease models and can confound the interpretation of biomarker levels and of patient responses to specific therapies. The complex nature of virtually all tissues has motivated the development of tools for single-cell genomic, transcriptomic and multiplex proteomic analyses. Here, we review these tools and assess their advantages and limitations. Emerging applications of single cell analysis tools in drug discovery and development, particularly in the field of oncology, are discussed.

Microfluidic enrichment for the single cell analysis of circulating tumor cells

Resistance to drug therapy is a major concern in cancer treatment. To probe clones resistant to chemotherapy, the current approach is to conduct pooled cell analysis. However, this can yield false negative outcomes, especially when we are analyzing a rare number of circulating tumor cells (CTCs) among an abundance of other cell types. Here, we develop a microfluidic device that is able to perform high throughput, selective picking and isolation of single CTC to 100% purity from a larger population of other cells. This microfluidic device can effectively separate the very rare CTCs from blood samples from as few as 1 in 20,000 white blood cells. We first demonstrate isolation of pure tumor cells from a mixed population and track variations of acquired T790M mutations before and after drug treatment using a model PC9 cell line. With clinical CTC samples, we then show that the isolated single CTCs are representative of dominant EGFR mutations such as T790M and L858R found in the primary tumor. With this single cell recovery device, we can potentially implement personalized treatment not only through detecting genetic aberrations at the single cell level, but also through tracking such changes during an anticancer therapy.

High-Throughput Single-Cell Cultivation on Microfluidic Streak Plates

This paper describes the microfluidic streak plate (MSP), a facile method for high-throughput microbial cell separation and cultivation in nanoliter sessile droplets. The MSP method builds upon the conventional streak plate technique by using microfluidic devices to generate nanoliter droplets that can be streaked manually or robotically onto petri dishes prefilled with carrier oil for cultivation of single cells. In addition, chemical gradients could be encoded in the droplet array for comprehensive dose-response analysis. The MSP method was validated by using single-cell isolation of Escherichia coli and antimicrobial susceptibility testing of Pseudomonas aeruginosa PAO1. The robustness of the MSP work flow was demonstrated by cultivating a soil community that degrades polycyclic aromatic hydrocarbons. Cultivation in droplets enabled detection of the richest species diversity with better coverage of rare species. Moreover, isolation and cultivation of bacterial strains by MSP led to the discovery of several species with high degradation efficiency, including four Mycobacterium isolates and a previously unknown fluoranthene-degrading Blastococcus species.

Gene expression variability in clonal populations: Causes and consequences

During the last decade it has been shown that among cell variation in gene expression plays an important role within clonal populations. Here, we provide an overview of the different mechanisms contributing to gene expression variability in clonal populations. These are ranging from inherent variations in the biochemical process of gene expression itself, such as intrinsic noise, extrinsic noise and bistability to individual responses to variations in the local micro-environment, a phenomenon called phenotypic plasticity. Also genotypic variations caused by clonal evolution and phase variation can contribute to gene expression variability. Consequently, gene expression studies need to take these fluctuations in expression into account. However, frequently used techniques for expression quantification, such as microarrays, RNA sequencing, quantitative PCR and gene reporter fusions classically determine the population average of gene expression. Here, we discuss how these techniques can be adapted towards single cell analysis by integration with single cell isolation, RNA amplification and microscopy. Alternatively more qualitative selection-based techniques, such as mutant screenings, in vivo expression technology (IVET) and recombination-based IVET (RIVET) can be applied for detection of genes expressed only within a subpopulation. Finally, differential fluorescence induction (DFI), a protocol specially designed for single cell expression is discussed.

ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis

Single-cell RNA-seq data allows insight into normal cellular function and various disease states through molecular characterization of gene expression on the single cell level. Dimensionality reduction of such high-dimensional data sets is essential for visualization and analysis, but single-cell RNA-seq data are challenging for classical dimensionality-reduction methods because of the prevalence of dropout events, which lead to zero-inflated data. Here, we develop a dimensionality-reduction method, (Z)ero (I)nflated (F)actor (A)nalysis (ZIFA), which explicitly models the dropout characteristics, and show that it improves modeling accuracy on simulated and biological data sets.

Tempo and mode of genomic mutations unveil human evolutionary history

Mutations that have occurred in human genomes provide insight into various aspects of evolutionary history such as speciation events and degrees of natural selection. Comparing genome sequences between human and great apes or among humans is a feasible approach for inferring human evolutionary history. Recent advances in high-throughput or so-called 'next-generation' DNA sequencing technologies have enabled the sequencing of thousands of individual human genomes, as well as a variety of reference genomes of hominids, many of which are publicly available. These sequence data can help to unveil the detailed demographic history of the lineage leading to humans as well as the explosion of modern human population size in the last several thousand years. In addition, high-throughput sequencing illustrates the tempo and mode of de novo mutations, which are producing human genetic variation at this moment. Pedigree-based human genome sequencing has shown that mutation rates vary significantly across the human genome. These studies have also provided an improved timescale of human evolution, because the mutation rate estimated from pedigree analysis is half that estimated from traditional analyses based on molecular phylogeny. Because of the dramatic reduction in sequencing cost, sequencing on-demand samples designed for specific studies is now also becoming popular. To produce data of sufficient quality to meet the requirements of the study, it is necessary to set an explicit sequencing plan that includes the choice of sample collection methods, sequencing platforms, and number of sequence reads.

Challenges of metagenomics and single-cell genomics approaches for exploring cyanobacterial diversity

Cyanobacteria have played a crucial role in the history of early earth and continue to be instrumental in shaping our planet, yet applications of cutting edge technology have not yet been widely used to explore cyanobacterial diversity. To provide adequate background, we briefly review current sequencing technologies and their innovative uses in genomics and metagenomics. Next, we focus on current cell capture technologies and the challenges of using them with cyanobacteria. We illustrate the utility in coupling breakthroughs in DNA amplification with cell capture platforms, with an example of microfluidic isolation and subsequent targeted amplicon sequencing from individual terrestrial thermophilic cyanobacteria. Single cells of thermophilic, unicellular Synechococcus sp. JA-2-3-B'a(2-13) (Syn OS-B') were sorted in a microfluidic device, lysed, and subjected to whole genome amplification by multiple displacement amplification. We amplified regions from specific CRISPR spacer arrays, which are known to be highly diverse, contain semi-palindromic repeats which form secondary structure, and can be difficult to amplify. Cell capture, lysis, and genome amplification on a microfluidic device have been optimized, setting a stage for further investigations of individual cyanobacterial cells isolated directly from natural populations.

Simple Bulk Readout of Digital Nucleic Acid Quantification Assays

Digital assays are powerful methods that enable detection of rare cells and counting of individual nucleic acid molecules. However, digital assays are still not routinely applied, due to the cost and specific equipment associated with commercially available methods. Here we present a simplified method for readout of digital droplet assays using a conventional real-time PCR instrument to measure bulk fluorescence of droplet-based digital assays. We characterize the performance of the bulk readout assay using synthetic droplet mixtures and a droplet digital multiple displacement amplification (MDA) assay. Quantitative MDA particularly benefits from a digital reaction format, but our new method applies to any digital assay. For established digital assay protocols such as digital PCR, this method serves to speed up and simplify assay readout. Our bulk readout methodology brings the advantages of partitioned assays without the need for specialized readout instrumentation. The principal limitations of the bulk readout methodology are reduced dynamic range compared with droplet-counting platforms and the need for a standard sample, although the requirements for this standard are less demanding than for a conventional real-time experiment. Quantitative whole genome amplification (WGA) is used to test for contaminants in WGA reactions and is the most sensitive way to detect the presence of DNA fragments with unknown sequences, giving the method great promise in diverse application areas including pharmaceutical quality control and astrobiology.

Technologies for Single-Cell Isolation

The handling of single cells is of great importance in applications such as cell line development or single-cell analysis, e.g., for cancer research or for emerging diagnostic methods. This review provides an overview of technologies that are currently used or in development to isolate single cells for subsequent single-cell analysis. Data from a dedicated online market survey conducted to identify the most relevant technologies, presented here for the first time, shows that FACS (fluorescence activated cell sorting) respectively Flow cytometry (33% usage), laser microdissection (17%), manual cell picking (17%), random seeding/dilution (15%), and microfluidics/lab-on-a-chip devices (12%) are currently the most frequently used technologies. These most prominent technologies are described in detail and key performance factors are discussed. The survey data indicates a further increasing interest in single-cell isolation tools for the coming years. Additionally, a worldwide patent search was performed to screen for emerging technologies that might become relevant in the future. In total 179 patents were found, out of which 25 were evaluated by screening the title and abstract to be relevant to the field.

Geometry of the Gene Expression Space of Individual Cells

There is a revolution in the ability to analyze gene expression of single cells in a tissue. To understand this data we must comprehend how cells are distributed in a high-dimensional gene expression space. One open question is whether cell types form discrete clusters or whether gene expression forms a continuum of states. If such a continuum exists, what is its geometry? Recent theory on evolutionary trade-offs suggests that cells that need to perform multiple tasks are arranged in a polygon or polyhedron (line, triangle, tetrahedron and so on, generally called polytopes) in gene expression space, whose vertices are the expression profiles optimal for each task. Here, we analyze single-cell data from human and mouse tissues profiled using a variety of single-cell technologies. We fit the data to shapes with different numbers of vertices, compute their statistical significance, and infer their tasks. We find cases in which single cells fill out a continuum of expression states within a polyhedron. This occurs in intestinal progenitor cells, which fill out a tetrahedron in gene expression space. The four vertices of this tetrahedron are each enriched with genes for a specific task related to stemness and early differentiation. A polyhedral continuum of states is also found in spleen dendritic cells, known to perform multiple immune tasks: cells fill out a tetrahedron whose vertices correspond to key tasks related to maturation, pathogen sensing and communication with lymphocytes. A mixture of continuum-like distributions and discrete clusters is found in other cell types, including bone marrow and differentiated intestinal crypt cells. This approach can be used to understand the geometry and biological tasks of a wide range of single-cell datasets. The present results suggest that the concept of cell type may be expanded. In addition to discreet clusters in gene-expression space, we suggest a new possibility: a continuum of states within a polyhedron, in which the vertices represent specialists at key tasks.

In the past, biological experiments usually pooled together millions of cells, masking the differences between individual cells. Current technology takes a big step forward by measuring gene expression from individual cells. Interpreting this data is challenging because we need to understand how cells are arranged in a high dimensional gene expression space. Here we test recent theory that suggests that cells facing multiple tasks should be arranged in simple low dimensional polygons or polyhedra (generally called polytopes). The vertices of the polytopes are gene expression profiles optimal for each of the tasks. We find evidence for such simplicity in a variety of tissues—spleen, bone marrow, intestine—analyzed by different single-cell technologies. We find that cells are distributed inside polytopes, such as tetrahedrons or four-dimensional simplexes, with cells closest to each vertex responsible for a different key task. For example, intestinal progenitor cells that give rise to the other cell types show a continuous distribution in a tetrahedron whose vertices correspond to several key sub-tasks. Immune dendritic cells likewise are continuously distributed between key immune tasks. This approach of testing whether data falls in polytopes may be useful for interpreting a variety of single-cell datasets in terms of biological tasks.