Priming chondrocytes during expansion alters cell behavior and improves matrix production in 3D culture.

OBJECTIVE: Cartilage tissue engineering strategies that use autologous chondrocytes require in vitro expansion of cells to obtain enough cells to produce functional engineered tissue. However, chondrocytes dedifferentiate during expansion culture, limiting their ability to produce chondrogenic tissue and their utility for cell-based cartilage repair strategies. The current study identified conditions that favor cartilage production and the mechanobiological mechanisms responsible for these benefits. DESIGN: Chondrocytes were isolated from juvenile bovine knee joints and cultured with (primed) or without (unprimed) a growth factor cocktail. Gene expression, cell morphology, cell adhesion, cytoskeletal protein distribution, and cell mechanics were assessed. Following passage 5, cells were embedded into agarose hydrogels to evaluate functional properties of engineered cartilage. RESULTS: Priming cells during expansion culture altered cell phenotype and chondrogenic tissue production. Unbiased ribonucleic acid-sequencing analysis suggested, and experimental studies confirmed, that growth factor priming delays dedifferentiation associated changes in cell adhesion and cytoskeletal organization. Priming also overrode mechanobiological pathways to prevent chondrocytes from remodeling their cytoskeleton to accommodate the stiff, monolayer microenvironment. Passage 1 primed cells deformed less and had lower yes associated protein 1 activity than unprimed cells. Differences in cell adhesion, morphology, and cell mechanics between primed and unprimed cells were mitigated by passage 5. CONCLUSIONS: Priming suppresses mechanobiologic cytoskeletal remodeling to prevent chondrocyte dedifferentiation, resulting in more cartilage-like tissue-engineered constructs.

A cooperative network at the nuclear envelope counteracts LINC-mediated forces during oogenesis in C. elegans.

Oogenesis involves transduction of mechanical forces from the cytoskeleton to the nuclear envelope (NE). In Caenorhabditis elegans, oocyte nuclei lacking the single lamin protein LMN-1 are vulnerable to collapse under forces mediated through LINC (linker of nucleoskeleton and cytoskeleton) complexes. Here, we use cytological analysis and in vivo imaging to investigate the balance of forces that drive this collapse and protect oocyte nuclei. We also use a mechano-node-pore sensing device to directly measure the effect of genetic mutations on oocyte nuclear stiffness. We find that nuclear collapse is not a consequence of apoptosis. It is promoted by dynein, which induces polarization of a LINC complex composed of Sad1 and UNC-84 homology 1 (SUN-1) and ZYGote defective 12 (ZYG-12). Lamins contribute to oocyte nuclear stiffness and cooperate with other inner nuclear membrane proteins to distribute LINC complexes and protect nuclei from collapse. We speculate that a similar network may protect oocyte integrity during extended oocyte arrest in mammals.

Mechano-Node-Pore Sensing: A Rapid, Label-Free Platform for Multi-Parameter Single-Cell Viscoelastic Measurements.

Cellular mechanical properties are involved in a wide variety of biological processes and diseases, ranging from stem cell differentiation to cancer metastasis. Conventional methods for measuring these properties, such as atomic force microscopy (AFM) and micropipette aspiration (MA), capture rich information, reflecting a cell's full viscoelastic response; however, these methods are limited by very low throughput. High-throughput approaches, such as real-time deformability cytometry (RT-DC), can only measure limited mechanical information, as they are often restricted to single-parameter readouts that only reflect a cell's elastic properties. In contrast to these methods, mechano-node-pore sensing (mechano-NPS) is a flexible, label-free microfluidic platform that bridges the gap in achieving multi-parameter viscoelastic measurements of a cell with moderate throughput. A direct current (DC) measurement is used to monitor cells as they transit a microfluidic channel, tracking their size and velocity before, during, and after they are forced through a narrow constriction. This information (i.e., size and velocity) is used to quantify each cell's transverse deformation, resistance to deformation, and recovery from deformation. In general, this electronics-based microfluidic platform provides multiple viscoelastic cell properties, and thus a more complete picture of a cell's mechanical state. Because it requires minimal sample preparation, utilizes a straightforward electronic measurement (in contrast to a high-speed camera), and takes advantage of standard soft lithography fabrication, the implementation of this platform is simple, accessible, and adaptable to downstream analysis. This platform's flexibility, utility, and sensitivity have provided unique mechanical information on a diverse range of cells, with the potential for many more applications in basic science and clinical diagnostics.

Detecting Intact Virus Using Exogenous Oligonucleotide Labels.

The COVID-19 pandemic has revealed how an emerging pathogen can cause a sudden and dramatic increase in demand for viral testing. Testing pooled samples could meet this demand; however, the sensitivity of reverse transcription quantitative polymerase chain reaction (RT-qPCR), the gold standard, significantly decreases with an increasing number of samples pooled. Here, we introduce detection of intact virus by exogenous-nucleotide reaction (DIVER), a method that quantifies intact virus and is robust to sample dilution. As demonstrated using two models of severe acute respiratory syndrome coronavirus 2, DIVER first tags membraned particles with exogenous oligonucleotides, then captures the tagged particles on beads functionalized with a virus-specific capture agent (in this instance, angiotensin-converting enzyme 2), and finally quantifies the oligonucleotide tags using qPCR. Using spike-presenting liposomes and spike-pseudotyped lentivirus, we show that DIVER can detect 1 × 105 liposomes and 100 plaque-forming units of lentivirus and can successfully identify positive samples in pooling experiments. Overall, DIVER is well positioned for efficient sample pooling and clinical validation.

Mechanical phenotyping reveals unique biomechanical responses in retinoic acid-resistant acute promyelocytic leukemia.

All-trans retinoic acid (ATRA) is an essential therapy in the treatment of acute promyelocytic leukemia (APL), but nearly 20% of patients with APL are resistant to ATRA. As there are no biomarkers for ATRA resistance that yet exist, we investigated whether cell mechanics could be associated with this pathological phenotype. Using mechano-node-pore sensing, a single-cell mechanical phenotyping platform, and patient-derived APL cell lines, we discovered that ATRA-resistant APL cells are less mechanically pliable. By investigating how different subcellular components of APL cells contribute to whole-cell mechanical phenotype, we determined that nuclear mechanics strongly influence an APL cell's mechanical response. Moreover, decondensing chromatin with trichostatin A is especially effective in softening ATRA-resistant APL cells. RNA-seq allowed us to compare the transcriptomic differences between ATRA-resistant and ATRA-responsive APL cells and highlighted gene expression changes that could be associated with mechanical changes. Overall, we have demonstrated the potential of "physical" biomarkers in identifying APL resistance.

Node-Pore Sensing for Characterizing Cells and Extracellular Vesicles.

Node-Pore Sensing, NPS, is an extremely versatile and powerful technique for the analysis of cells and the detection of extracellular vesicles (EVs). NPS involves measuring the modulated current pulse caused by a cell transiting a microfluidic channel that has been segmented by a series of inserted nodes. As the current pulse reflects the number of nodes and segments of the channel, NPS can achieve exquisite sensitivity. Thus, when used as a Coulter counter, NPS can measure the sub-micron size increase of antibody-coated colloids to which EVs are specifically bound. By simply inserting between two nodes a "contraction" channel through which cells can squeeze, one can mechanically phenotype cells. We discuss the details of performing these two NPS applications.

Evaluating sources of technical variability in the mechano-node-pore sensing pipeline and their effect on the reproducibility of single-cell mechanical phenotyping.

Cellular mechanical properties can reveal physiologically relevant characteristics in many cell types, and several groups have developed microfluidics-based platforms to perform high-throughput single-cell mechanical testing. However, prior work has performed only limited characterization of these platforms' technical variability and reproducibility. Here, we evaluate the repeatability performance of mechano-node-pore sensing, a single-cell mechanical phenotyping platform developed by our research group. We measured the degree to which device-to-device variability and semi-manual data processing affected this platform's measurements of single-cell mechanical properties. We demonstrated high repeatability across the entire technology pipeline even for novice users. We then compared results from identical mechano-node-pore sensing experiments performed by researchers in two different laboratories with different analytical instruments, demonstrating that the mechanical testing results from these two locations are in agreement. Our findings quantify the expectation of technical variability in mechano-node-pore sensing even in minimally experienced hands. Most importantly, we find that the repeatability performance we measured is fully sufficient for interpreting biologically relevant single-cell mechanical measurements with high confidence.

DNA-Directed Patterning for Versatile Validation and Characterization of a Lipid-Based Nanoparticle Model of SARS-CoV-2.

Lipid-based nanoparticles have been applied extensively in drug delivery and vaccine strategies and are finding diverse applications in the coronavirus disease 2019 (COVID-19) pandemic-from vaccine-component encapsulation to modeling the virus, itself. High-throughput, highly flexible methods for characterization are of great benefit to the development of liposomes featuring surface proteins. DNA-directed patterning is one such method that offers versatility in immobilizing and segregating lipid-based nanoparticles for subsequent analysis. Here, oligonucleotides are selectively conjugated onto a glass substrate and then hybridized to complementary oligonucleotides tagged to liposomes, patterning them with great control and precision. The power of DNA-directed patterning is demonstrated by characterizing a novel recapitulative lipid-based nanoparticle model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-S-liposomes-that presents the SARS-CoV-2 spike (S) protein on its surface. Patterning a mixture of S-liposomes and liposomes that display the tetraspanin CD63 to discrete regions of a substrate shows that angiotensin-converting enzyme 2 (ACE2) specifically binds to S-liposomes. Subsequent introduction of S-liposomes to ACE2-expressing cells tests the biological function of S-liposomes and shows agreement with DNA-directed patterning-based assays. Finally, multiplexed patterning of S-liposomes verifies the performance of commercially available neutralizing antibodies against the two S variants. Overall, DNA-directed patterning enables a wide variety of custom assays for the characterization of any lipid-based nanoparticle.

Multiplexed DNA-Directed Patterning of Antibodies for Applications in Cell Subpopulation Analysis.

Antibodies provide the functional biospecificity that has enabled the development of sensors, diagnostic tools, and assays in both laboratory and clinical settings. However, as multimarker screening becomes increasingly necessary due to the heterogeneity and complexity of human pathology, new methods must be developed that are capable of coordinating the precise assembly of multiple, distinct antibodies. To address this technological challenge, we engineered a bottom-up, high-throughput method in which DNA patterns, comprising unique 20-base pair oligonucleotides, are patterned onto a substrate using photolithography. These microfabricated surface patterns are programmed to hybridize with, and instruct the multiplexed assembly of, antibodies conjugated with the complementary DNA strands. We demonstrate that this simple, yet robust, approach preserves the antibody-binding functionality in two common applications: antibody-based cell capture and label-free surface marker screening. Using a simple proof-of-concept capture device, we achieved high purity separation of a breast cancer cell line, MCF-7, from a blood cell line, Jurkat, with capture purities of 77.4% and 96.6% when using antibodies specific for the respective cell types. We also show that antigen-antibody interactions slow cell trajectories in flow in the next-generation microfluidic node-pore sensing (NPS) device, enabling the differentiation of MCF-7 and Jurkat cells based on EpCAM surface-marker expression. Finally, we use a next-generation NPS device patterned with antibodies against E-cadherin, N-cadherin, and ß-integrin-three markers that are associated with epithelial-mesenchymal transitions-to perform label-free surface marker screening of MCF10A, MCF-7, and Hs 578T breast epithelial cells. Our high-throughput, highly versatile technique enables rapid development of customized, antibody-based assays across a host of diverse diseases and research thrusts.

Deep proteome profiling of human mammary epithelia at lineage and age resolution.

Age is the major risk factor in most carcinomas, yet little is known about how proteomes change with age in any human epithelium. We present comprehensive proteomes comprised of >9,000 total proteins and >15,000 phosphopeptides from normal primary human mammary epithelia at lineage resolution from ten women ranging in age from 19 to 68 years. Data were quality controlled and results were biologically validated with cell-based assays. Age-dependent protein signatures were identified using differential expression analyses and weighted protein co-expression network analyses. Upregulation of basal markers in luminal cells, including KRT14 and AXL, were a prominent consequence of aging. PEAK1 was identified as an age-dependent signaling kinase in luminal cells, which revealed a potential age-dependent vulnerability for targeted ablation. Correlation analyses between transcriptome and proteome revealed age-associated loss of proteostasis regulation. Age-dependent proteome changes in the breast epithelium identified heretofore unknown potential therapeutic targets for reducing breast cancer susceptibility.

Toward Community Surveillance: Detecting Intact SARS-CoV-2 Using Exogeneous Oligonucleotide Labels.

The persistence of the COVID-19 pandemic demands a dramatic increase in testing efficiency. Testing pooled samples for SARS-CoV-2 could meet this need; however, the sensitivity of RT-qPCR, the gold standard, significantly decreases with an increasing number of samples pooled. Here, we introduce DIVER, a method that quantifies intact virus and is robust to sample dilution. DIVER first tags viral particles with exogeneous oligonucleotides, then captures the tagged particles on ACE2-functionalized beads, and finally quantifies the oligonucleotide tags using qPCR. Using spike-presenting liposomes and Spike-pseudotyped lentivirus as SARS-CoV-2 models, we show that DIVER can detect 1×10 5 liposomes and 100 pfu lentivirus and can successfully identify positive samples in pooling experiments. Overall, DIVER is well-positioned for efficient sample pooling and expanded community surveillance.

Simple, Affordable, and Modular Patterning of Cells using DNA.

The relative positioning of cells is a key feature of the microenvironment that organizes cell-cell interactions. To study the interactions between cells of the same or different type, micropatterning techniques have proved useful. DNA Programmed Assembly of Cells (DPAC) is a micropatterning technique that targets the adhesion of cells to a substrate or other cells using DNA hybridization. The most basic operations in DPAC begin with decorating cell membranes with lipid-modified oligonucleotides, then flowing them over a substrate that has been patterned with complementary DNA sequences. Cells adhere selectively to the substrate only where they find a complementary DNA sequence. Non-adherent cells are washed away, revealing a pattern of adherent cells. Additional operations include further rounds of cell-substrate or cell-cell adhesion, as well as transferring the patterns formed by DPAC to an embedding hydrogel for long-term culture. Previously, methods for patterning oligonucleotides on surfaces and decorating cells with DNA sequences required specialized equipment and custom DNA synthesis, respectively. We report an updated version of the protocol, utilizing an inexpensive benchtop photolithography setup and commercially available cholesterol modified oligonucleotides (CMOs) deployed using a modular format. CMO-labeled cells adhere with high efficiency to DNA-patterned substrates. This approach can be used to pattern multiple cell types at once with high precision and to create arrays of microtissues embedded within an extracellular matrix. Advantages of this method include its high resolution, ability to embed cells into a three-dimensional microenvironment without disrupting the micropattern, and flexibility in patterning any cell type.

How Can Microfluidic and Microfabrication Approaches Make Experiments More Physiologically Relevant?

Microfabricated and microfluidic devices enable standardized handling, precise spatiotemporal manipulation of cells and liquids, and recapitulation of cellular environments, tissues, and organ-level biology. We asked researchers how these devices can make in vitro experiments more physiologically relevant.

The promise of single-cell mechanophenotyping for clinical applications.

Cancer is the second leading cause of death worldwide. Despite the immense research focused in this area, one is still not able to predict disease trajectory. To overcome shortcomings in cancer disease study and monitoring, we describe an exciting research direction: cellular mechanophenotyping. Cancer cells must overcome many challenges involving external forces from neighboring cells, the extracellular matrix, and the vasculature to survive and thrive. Identifying and understanding their mechanical behavior in response to these forces would advance our understanding of cancer. Moreover, used alongside traditional methods of immunostaining and genetic analysis, mechanophenotyping could provide a comprehensive view of a heterogeneous tumor. In this perspective, we focus on new technologies that enable single-cell mechanophenotyping. Single-cell analysis is vitally important, as mechanical stimuli from the environment may obscure the inherent mechanical properties of a cell that can change over time. Moreover, bulk studies mask the heterogeneity in mechanical properties of single cells, especially those rare subpopulations that aggressively lead to cancer progression or therapeutic resistance. The technologies on which we focus include atomic force microscopy, suspended microchannel resonators, hydrodynamic and optical stretching, and mechano-node pore sensing. These technologies are poised to contribute to our understanding of disease progression as well as present clinical opportunities.

Recapitulating complex biological signaling environments using a multiplexed, DNA-patterning approach.

Elucidating how the spatial organization of extrinsic signals modulates cell behavior and drives biological processes remains largely unexplored because of challenges in controlling spatial patterning of multiple microenvironmental cues in vitro. Here, we describe a high-throughput method that directs simultaneous assembly of multiple cell types and solid-phase ligands across length scales within minutes. Our method involves lithographically defining hierarchical patterns of unique DNA oligonucleotides to which complementary strands, attached to cells and ligands-of-interest, hybridize. Highlighting our method's power, we investigated how the spatial presentation of self-renewal ligand fibroblast growth factor-2 (FGF-2) and differentiation signal ephrin-B2 instruct single adult neural stem cell (NSC) fate. We found that NSCs have a strong spatial bias toward FGF-2 and identified an unexpected subpopulation exhibiting high neuronal differentiation despite spatially occupying patterned FGF-2 regions. Overall, our broadly applicable, DNA-directed approach enables mechanistic insight into how tissues encode regulatory information through the spatial presentation of heterogeneous signals.

Patterning the Geometry of Human Embryonic Stem Cell Colonies on Compliant Substrates to Control Tissue-Level Mechanics.

Human embryonic stem cells demonstrate a unique ability to respond to morphogens in vitro by self-organizing patterns of cell fate specification that correspond to primary germ layer formation during embryogenesis. Thus, these cells represent a powerful tool with which to examine the mechanisms that drive early human development. We have developed a method to culture human embryonic stem cells in confined colonies on compliant substrates that provides control over both the geometry of the colonies and their mechanical environment in order to recapitulate the physical parameters that underlie embryogenesis. The key feature of this method is the ability to generate polyacrylamide hydrogels with defined patterns of extracellular matrix ligand at the surface to promote cell attachment. This is achieved by fabricating stencils with the desired geometric patterns, using these stencils to create patterns of extracellular matrix ligand on glass coverslips, and transferring these patterns to polyacrylamide hydrogels during polymerization. This method is also compatible with traction force microscopy, allowing the user to measure and map the distribution of cell-generated forces within the confined colonies. In combination with standard biochemical assays, these measurements can be used to examine the role mechanical cues play in fate specification and morphogenesis during early human development.

Visco-Node-Pore Sensing: A Microfluidic Rheology Platform to Characterize Viscoelastic Properties of Epithelial Cells.

Viscoelastic properties of cells provide valuable information regarding biological or clinically relevant cellular characteristics. Here, we introduce a new, electronic-based, microfluidic platform-visco-node-pore sensing (visco-NPS)-which quantifies cellular viscoelastic properties under periodic deformation. We measure the storage (G') and loss (G″) moduli (i.e., elasticity and viscosity, respectively) of cells. By applying a wide range of deformation frequencies, our platform quantifies the frequency dependence of viscoelastic properties. G' and G″ measurements show that the viscoelastic properties of malignant breast epithelial cells (MCF-7) are distinctly different from those of non-malignant breast epithelial cells (MCF-10A). With its sensitivity, visco-NPS can dissect the individual contributions of different cytoskeletal components to whole-cell mechanical properties. Moreover, visco-NPS can quantify the mechanical transitions of cells as they traverse the cell cycle or are initiated into an epithelial-mesenchymal transition. Visco-NPS identifies viscoelastic characteristics of cell populations, providing a biophysical understanding of cellular behavior and a potential for clinical applications.

Developments in label-free microfluidic methods for single-cell analysis and sorting.

Advancements in microfluidic technologies have led to the development of many new tools for both the characterization and sorting of single cells without the need for exogenous labels. Label-free microfluidics reduce the preparation time, reagents needed, and cost of conventional methods based on fluorescent or magnetic labels. Furthermore, these devices enable analysis of cell properties such as mechanical phenotype and dielectric parameters that cannot be characterized with traditional labels. Some of the most promising technologies for current and future development toward label-free, single-cell analysis and sorting include electronic sensors such as Coulter counters and electrical impedance cytometry; deformation analysis using optical traps and deformation cytometry; hydrodynamic sorting such as deterministic lateral displacement, inertial focusing, and microvortex trapping; and acoustic sorting using traveling or standing surface acoustic waves. These label-free microfluidic methods have been used to screen, sort, and analyze cells for a wide range of biomedical and clinical applications, including cell cycle monitoring, rapid complete blood counts, cancer diagnosis, metastatic progression monitoring, HIV and parasite detection, circulating tumor cell isolation, and point-of-care diagnostics. Because of the versatility of label-free methods for characterization and sorting, the low-cost nature of microfluidics, and the rapid prototyping capabilities of modern microfabrication, we expect this class of technology to continue to be an area of high research interest going forward. New developments in this field will contribute to the ongoing paradigm shift in cell analysis and sorting technologies toward label-free microfluidic devices, enabling new capabilities in biomedical research tools as well as clinical diagnostics. This article is categorized under: Diagnostic Tools > Biosensing Diagnostic Tools > Diagnostic Nanodevices.

Characterizing cellular mechanical phenotypes with mechano-node-pore sensing.

The mechanical properties of cells change with their differentiation, chronological age, and malignant progression. Consequently, these properties may be useful label-free biomarkers of various functional or clinically relevant cell states. Here, we demonstrate mechano-node-pore sensing (mechano-NPS), a multi-parametric single-cell-analysis method that utilizes a four-terminal measurement of the current across a microfluidic channel to quantify simultaneously cell diameter, resistance to compressive deformation, transverse deformation under constant strain, and recovery time after deformation. We define a new parameter, the whole-cell deformability index (wCDI), which provides a quantitative mechanical metric of the resistance to compressive deformation that can be used to discriminate among different cell types. The wCDI and the transverse deformation under constant strain show malignant MCF-7 and A549 cell lines are mechanically distinct from non-malignant, MCF-10A and BEAS-2B cell lines, and distinguishes between cells treated or untreated with cytoskeleton-perturbing small molecules. We categorize cell recovery time, ΔTr, as instantaneous (ΔTr ~ 0 ms), transient (ΔTr ≤ 40ms), or prolonged (ΔTr > 40ms), and show that the composition of recovery types, which is a consequence of changes in cytoskeletal organization, correlates with cellular transformation. Through the wCDI and cell-recovery time, mechano-NPS discriminates between sub-lineages of normal primary human mammary epithelial cells with accuracy comparable to flow cytometry, but without antibody labeling. Mechano-NPS identifies mechanical phenotypes that distinguishes lineage, chronological age, and stage of malignant progression in human epithelial cells.

Single cell clustering based on cell-pair differentiability correlation and variance analysis.

Motivation: The rapid advancement of single cell technologies has shed new light on the complex mechanisms of cellular heterogeneity. Identification of intercellular transcriptomic heterogeneity is one of the most critical tasks in single-cell RNA-sequencing studies. Results: We propose a new cell similarity measure based on cell-pair differentiability correlation, which is derived from gene differential pattern among all cell pairs. Through plugging into the framework of hierarchical clustering with this new measure, we further develop a variance analysis based clustering algorithm 'Corr' that can determine cluster number automatically and identify cell types accurately. The robustness and superiority of the proposed algorithm are compared with representative algorithms: shared nearest neighbor (SNN)-Cliq and several other state-of-the-art clustering methods, on many benchmark or real single cell RNA-sequencing datasets in terms of both internal criteria (clustering number and accuracy) and external criteria (purity, adjusted rand index, F1-measure). Moreover, differentiability vector with our new measure provides a new means in identifying potential biomarkers from cancer related single cell datasets even with strong noise. Prognosis analyses from independent datasets of cancers confirmed the effectiveness of our 'Corr' method. Availability and implementation: The source code (Matlab) is available at http://sysbio.sibcb.ac.cn/cb/chenlab/soft/Corr--SourceCodes.zip. Supplementary information: Supplementary data are available at Bioinformatics online.