Intercellular signaling through secreted proteins induces free-energy gradient-directed cell movement

Kinetic Trajectories of Glucose Uptake in Single Cancer Cells Reveal a Drug-Induced Cell-State Change Within Hours of Drug Treatment

The development of drug resistance is a nearly universal phenomenon in patients with glioblastoma multiforme (GBM) brain tumors. Upon treatment, GBM cancer cells may initially undergo a drug-induced cell-state change to a drug-tolerant, slow-cycling state. The kinetics of that process are not well understood, in part due to the heterogeneity of GBM tumors and tumor models, which can confound the interpretation of kinetic data. Here, we resolve drug-adaptation kinetics in a patient-derived in vitro GBM tumor model characterized by the epithelial growth factor receptor (EGFR) variant(v)III oncogene treated with an EGFR inhibitor. We use radiolabeled 18F-fluorodeoxyglucose (FDG) to monitor the glucose uptake trajectories of single GBM cancer cells over a 12 h period of drug treatment. Autocorrelation analysis of the single-cell glucose uptake trajectories reveals evidence of a drug-induced cell-state change from a high- to low-glycolytic phenotype after 5-7 h of drug treatment. Information theoretic analysis of a bulk transcriptome kinetic series of the GBM tumor model delineated the underlying molecular mechanisms driving the cellular state change, including a shift from a stem-like mesenchymal state to a more differentiated, slow-cycling astrocyte-like state. Our results demonstrate that complex drug-induced cancer cell-state changes of cancer cells can be captured via measurements of single cell metabolic trajectories and reveal the extremely facile nature of drug adaptation.

A random forest model for predicting exosomal proteins using evolutionary information and motifs

Non-invasive diagnostics and therapies are crucial to prevent patients from undergoing painful procedures. Exosomal proteins can serve as important biomarkers for such advancements. In this study, we attempted to build a model to predict exosomal proteins. All models are trained, tested, and evaluated on a non-redundant dataset comprising 2831 exosomal and 2831 non-exosomal proteins, where no two proteins have more than 40% similarity. Initially, the standard similarity-based method Basic Local Alignment Search Tool (BLAST) was used to predict exosomal proteins, which failed due to low-level similarity in the dataset. To overcome this challenge, machine learning (ML) based models were developed using compositional and evolutionary features of proteins achieving an area under the receiver operating characteristics (AUROC) of 0.73. Our analysis also indicated that exosomal proteins have a variety of sequence-based motifs which can be used to predict exosomal proteins. Hence, we developed a hybrid method combining motif-based and ML-based approaches for predicting exosomal proteins, achieving a maximum AUROC of 0.85 and MCC of 0.56 on an independent dataset. This hybrid model performs better than presently available methods when assessed on an independent dataset. A web server and a standalone software ExoProPred (https://webs.iiitd.edu.in/raghava/exopropred/) have been created to help scientists predict and discover exosomal proteins and find functional motifs present in them.

The immunogenic radiation and new players in immunotherapy and targeted therapy for head and neck cancer

Although treatment modalities for head and neck cancer have evolved considerably over the past decades, survival rates have plateaued. The treatment options remained limited to definitive surgery, surgery followed by fractionated radiotherapy with optional chemotherapy, and a definitive combination of fractionated radiotherapy and chemotherapy. Lately, immunotherapy has been introduced as the fourth modality of treatment, mainly administered as a single checkpoint inhibitor for recurrent or metastatic disease. While other regimens and combinations of immunotherapy and targeted therapy are being tested in clinical trials, adapting the appropriate regimens to patients and predicting their outcomes have yet to reach the clinical setting. Radiotherapy is mainly regarded as a means to target cancer cells while minimizing the unwanted peripheral effect. Radiotherapy regimens and fractionation are designed to serve this purpose, while the systemic effect of radiation on the immune response is rarely considered a factor while designing treatment. To bridge this gap, this review will highlight the effect of radiotherapy on the tumor microenvironment locally, and the immune response systemically. We will review the methodology to identify potential targets for therapy in the tumor microenvironment and the scientific basis for combining targeted therapy and radiotherapy. We will describe a current experience in preclinical models to test these combinations and propose how challenges in this realm may be faced. We will review new players in targeted therapy and their utilization to drive immunogenic response against head and neck cancer. We will outline the factors contributing to head and neck cancer heterogeneity and their effect on the response to radiotherapy. We will review in-silico methods to decipher intertumoral and intratumoral heterogeneity and how these algorithms can predict treatment outcomes. We propose that (a) the sequence of surgery, radiotherapy, chemotherapy, and targeted therapy should be designed not only to annul cancer directly, but to prime the immune response. (b) Fractionation of radiotherapy and the extent of the irradiated field should facilitate systemic immunity to develop. (c) New players in targeted therapy should be evaluated in translational studies toward clinical trials. (d) Head and neck cancer treatment should be personalized according to patients and tumor-specific factors.

Deciphering Cell-Cell Interactions with Integrative Single-Cell Secretion Profiling

Cell-cell interactions are the fundamental behaviors to regulate cellular activities. A comprehensive evaluation of intercellular interactions requires direct profiling of various signaling behaviors simultaneously at the single-cell level, which remains lacking. Herein, an integrative single-cell secretion analysis platform is presented to profile different secreted factors (four proteins, three extracellular vesicles (EV) phenotypes), spatial distances, and migration information (distances and direction) simultaneously from high-throughput paired single cells using an antibody-barcode microchip. Applying the platform to analyze the tumor-stromal and tumor-immune interactions with the human oral squamous cell carcinoma (OSCC) cell lines and primary OSCC cells reveals that the initial distances between cells would determine their migratory distances and direction to approach stable organization. The cell-cell in close proximity enhances protein secretions while attenuating EV secretions. Migration has a more profound correlation with protein secretions than EV secretions, in which absolute migration distance affects protein secretions significantly but not the direction. These findings highlight the significance of spatial organization in regulating cell signaling behaviors and demonstrate that the integrative single-cell secretion profiling platform is well-suited for a comprehensive dissection of intercellular communication and interactions, providing new avenues for understanding cell-cell interaction biology and how different signaling behaviors coordinate within the tumor microenvironment.

Recent advances in microfluidics for single-cell functional proteomics

Single-cell proteomics (SCP) reveals phenotypic heterogeneity by profiling individual cells, their biological states and functional outcomes upon signaling activation that can hardly be probed via other omics characterizations. This has become appealing to researchers as it enables an overall more holistic view of biological details underlying cellular processes, disease onset and progression, as well as facilitates unique biomarker identification from individual cells. Microfluidic-based strategies have become methods of choice for single-cell analysis because they allow facile assay integrations, such as cell sorting, manipulation, and content analysis. Notably, they have been serving as an enabling technology to improve the sensitivity, robustness, and reproducibility of recently developed SCP methods. Critical roles of microfluidics technologies are expected to further expand rapidly in advancing the next phase of SCP analysis to reveal more biological and clinical insights. In this review, we will capture the excitement of the recent achievements of microfluidics methods for both targeted and global SCP, including efforts to enhance the proteomic coverage, minimize sample loss, and increase multiplexity and throughput. Furthermore, we will discuss the advantages, challenges, applications, and future prospects of SCP.

Computational quantification and characterization of independently evolving cellular subpopulations within tumors is critical to inhibit anti-cancer therapy resistance

BACKGROUND

Drug resistance continues to be a major limiting factor across diverse anti-cancer therapies. Contributing to the complexity of this challenge is cancer plasticity, in which one cancer subtype switches to another in response to treatment, for example, triple-negative breast cancer (TNBC) to Her2-positive breast cancer. For optimal treatment outcomes, accurate tumor diagnosis and subsequent therapeutic decisions are vital. This study assessed a novel approach to characterize treatment-induced evolutionary changes of distinct tumor cell subpopulations to identify and therapeutically exploit anticancer drug resistance.

METHODS

In this research, an information-theoretic single-cell quantification strategy was developed to provide a high-resolution and individualized assessment of tumor composition for a customized treatment approach. Briefly, this single-cell quantification strategy computes cell barcodes based on at least 100,000 tumor cells from each experiment and reveals a cell-specific signaling signature (CSSS) composed of a set of ongoing processes in each cell.

RESULTS

Using these CSSS-based barcodes, distinct subpopulations evolving within the tumor in response to an outside influence, like anticancer treatments, were revealed and mapped. Barcodes were further applied to assign targeted drug combinations to each individual tumor to optimize tumor response to therapy. The strategy was validated using TNBC models and patient-derived tumors known to switch phenotypes in response to radiotherapy (RT).

CONCLUSIONS

We show that a barcode-guided targeted drug cocktail significantly enhances tumor response to RT and prevents regrowth of once-resistant tumors. The strategy presented herein shows promise in preventing cancer treatment resistance, with significant applicability in clinical use.

Toward Rapid and Automated Immunoassays: Using a Localized Electrochemical pH Modulation Platform to Perform a Single-Step Immunoassay

An electrochemical platform for generating and controlling a localized pH microenvironment on demand is proposed by employing a closed-loop control algorithm based on an iridium oxide pH sensor input. We use a combination of solution-borne quinones and galvanostatic excitation on a prepatterned indium tin oxide (ITO) working electrode to modulate pH within a very well confined, small volume of solution close to the electrode surface. We demonstrate that the rate of pH change can be controlled at up to 2 pH s^-1 with an excellent repeatability (±0.004). The desired pH microenvironment can be stably maintained for longer than 2 h within ±0.0012 pH. As a high-impact application of the platform technology, we propose a single-step immunoassay and demonstrate its utility in measuring C-reactive protein (CRP), a critical inflammatory marker in various conditions such as myocardial infarction and even SARS-Cov-2. Utilizing pH modulation technology along with pH-sensitive fluorescence dye simplifies the immunoassay process into a single-step, where a mixture of all of the reagents is incubated only for 1 h without any washing steps or the need to change solution. This simplified immunoassay process minimizes the hands-on time of the end-user and thus decreases technician-driven errors. Moreover, the absence of complicated liquid-handling hardware makes it more suitable and attractive for an ultracompact platform to ultimately be used in a point-of-care diagnostic assay.

The Intriguing Landscape of Single-Cell Protein Analysis

Profiling protein expression at single-cell resolution is essential for fundamental biological research (such as cell differentiation and tumor microenvironmental examination) and clinical precision medicine where only a limited number of primary cells are permitted. With the recent advances in engineering, chemistry, and biology, single-cell protein analysis methods are developed rapidly, which enable high-throughput and multiplexed protein measurements in thousands of individual cells. In combination with single cell RNA sequencing and mass spectrometry, single-cell multi-omics analysis can simultaneously measure multiple modalities including mRNAs, proteins, and metabolites in single cells, and obtain a more comprehensive exploration of cellular signaling processes, such as DNA modifications, chromatin accessibility, protein abundance, and gene perturbation. Here, the recent progress and applications of single-cell protein analysis technologies in the last decade are summarized. Current limitations, challenges, and possible future directions in this field are also discussed.

Measuring single-cell protein secretion in immunology: Technologies, advances, and applications

The dynamics, nature, strength, and ultimately protective capabilities of an active immune response are determined by the extracellular constitution and concentration of various soluble factors. Generated effector cells secrete such mediators, including antibodies, chemo‐ and cytokines to achieve functionality. These secreted factors organize the individual immune cells into functional tissues, initiate, orchestrate, and regulate the immune response. Therefore, a single‐cell resolved analysis of protein secretion is a valuable tool for studying the heterogeneity and functionality of immune cells. This review aims to provide a comparative overview of various methods to characterize immune reactions by measuring single‐cell protein secretion. Spot‐based and cytometry‐based assays, such as ELISpot and flow cytometry, respectively, are well‐established methods applied in basic research and clinical settings. Emerging novel technologies, such as microfluidic platforms, offer new ways to measure and exploit protein secretion in immune reactions. Further technological advances will allow the deciphering of protein secretion in immunological responses with unprecedented detail, linking secretion to functionality. Here, we summarize the development and recent advances of tools that allow the analysis of protein secretion at the single‐cell level, and discuss and contrast their applications within immunology.

Single-Cell Proteomics

The inability to make broad, minimally biased measurements of a cell's proteome stands as a major bottleneck for understanding how gene expression translates into cellular phenotype. Unlike sequencing for nucleic acids, there is no dominant method for making single-cell proteomic measurements. Instead, methods typically focus on either absolute quantification of a small number of proteins or highly multiplexed protein measurements. Advances in microfluidics and output encoding have led to major improvements in both aspects. Here, we review the most recent progress that has enabled hundreds of protein measurements and ultrahigh-sensitivity quantification. We also highlight emerging technologies such as single-cell mass spectrometry that may enable unbiased measurement of cellular proteomes.

Thermodynamic energetics underlying genomic instability and whole-genome doubling in cancer

Genomic instability contributes to tumorigenesis through the amplification and deletion of cancer driver genes. DNA copy number (CN) profiling of ensembles of tumors allows a thermodynamic analysis of the profile for each tumor. The free energy of the distribution of CNs is found to be a monotonically increasing function of the average chromosomal ploidy. The dependence is universal across several cancer types. Surprisal analysis distinguishes two main known subgroups: tumors with cells that have or have not undergone whole-genome duplication (WGD). The analysis uncovers that CN states having a narrower distribution are energetically more favorable toward the WGD transition. Surprisal analysis also determines the deviations from a fully stable-state distribution. These deviations reflect constraints imposed by tumor fitness selection pressures. The results point to CN changes that are more common in high-ploidy tumors and thus support altered selection pressures upon WGD.

Multiomic Immunophenotyping of COVID-19 Patients Reveals Early Infection Trajectories

Host immune responses play central roles in controlling SARS-CoV2 infection, yet remain incompletely characterized and understood. Here, we present a comprehensive immune response map spanning 454 proteins and 847 metabolites in plasma integrated with single-cell multi-omic assays of PBMCs in which whole transcriptome, 192 surface proteins, and T and B cell receptor sequence were co-analyzed within the context of clinical measures from 50 COVID19 patient samples. Our study reveals novel cellular subpopulations, such as proliferative exhausted CD8 ⁺ and CD4 ⁺ T cells, and cytotoxic CD4 ⁺ T cells, that may be features of severe COVID-19 infection. We condensed over 1 million immune features into a single immune response axis that independently aligns with many clinical features and is also strongly associated with disease severity. Our study represents an important resource towards understanding the heterogeneous immune responses of COVID-19 patients and may provide key information for informing therapeutic development.

Advances of Single-Cell Protein Analysis

Proteins play a significant role in the key activities of cells. Single-cell protein analysis provides crucial insights in studying cellular heterogeneities. However, the low abundance and enormous complexity of the proteome posit challenges in analyzing protein expressions at the single-cell level. This review summarizes recent advances of various approaches to single-cell protein analysis. We begin by discussing conventional characterization approaches, including fluorescence flow cytometry, mass cytometry, enzyme-linked immunospot assay, and capillary electrophoresis. We then detail the landmark advances of microfluidic approaches for analyzing single-cell protein expressions, including microfluidic fluorescent flow cytometry, droplet-based microfluidics, microwell-based assay (microengraving), microchamber-based assay (barcoding microchips), and single-cell Western blotting, among which the advantages and limitations are compared. Looking forward, we discuss future research opportunities and challenges for multiplexity, analyte, throughput, and sensitivity of the microfluidic approaches, which we believe will prompt the research of single-cell proteins such as the molecular mechanism of cell biology, as well as the clinical applications for tumor treatment and drug development.

Multi-omic single-cell snapshots reveal multiple independent trajectories to drug tolerance in a melanoma cell line

The determination of individual cell trajectories through a high-dimensional cell-state space is an outstanding challenge for understanding biological changes ranging from cellular differentiation to epigenetic responses of diseased cells upon drugging. We integrate experiments and theory to determine the trajectories that single BRAFV600E mutant melanoma cancer cells take between drug-naive and drug-tolerant states. Although single-cell omics tools can yield snapshots of the cell-state landscape, the determination of individual cell trajectories through that space can be confounded by stochastic cell-state switching. We assayed for a panel of signaling, phenotypic, and metabolic regulators at points across 5 days of drug treatment to uncover a cell-state landscape with two paths connecting drug-naive and drug-tolerant states. The trajectory a given cell takes depends upon the drug-naive level of a lineage-restricted transcription factor. Each trajectory exhibits unique druggable susceptibilities, thus updating the paradigm of adaptive resistance development in an isogenic cell population.

Exploring Alzheimer's Disease Molecular Variability via Calculation of Personalized Transcriptional Signatures

Despite huge investments and major efforts to develop remedies for Alzheimer’s disease (AD) in the past decades, AD remains incurable. While evidence for molecular and phenotypic variability in AD have been accumulating, AD research still heavily relies on the search for AD-specific genetic/protein biomarkers that are expected to exhibit repetitive patterns throughout all patients. Thus, the classification of AD patients to different categories is expected to set the basis for the development of therapies that will be beneficial for subpopulations of patients. Here we explore the molecular heterogeneity among a large cohort of AD and non-demented brain samples, aiming to address the question whether AD-specific molecular biomarkers can progress our understanding of the disease and advance the development of anti-AD therapeutics. We studied 951 brain samples, obtained from up to 17 brain regions of 85 AD patients and 22 non-demented subjects. Utilizing an information-theoretic approach, we deciphered the brain sample-specific structures of altered transcriptional networks. Our in-depth analysis revealed that 7 subnetworks were repetitive in the 737 diseased and 214 non-demented brain samples. Each sample was characterized by a subset consisting of ~1–3 subnetworks out of 7, generating 52 distinct altered transcriptional signatures that characterized the 951 samples. We show that 30 different altered transcriptional signatures characterized solely AD samples and were not found in any of the non-demented samples. In contrast, the rest of the signatures characterized different subsets of sample types, demonstrating the high molecular variability and complexity of gene expression in AD. Importantly, different AD patients exhibiting similar expression levels of AD biomarkers harbored distinct altered transcriptional networks. Our results emphasize the need to expand the biomarker-based stratification to patient-specific transcriptional signature identification for improved AD diagnosis and for the development of subclass-specific future treatment.

Translating Cancer Molecular Variability into Personalized Information Using Bulk and Single Cell Approaches

Cancer research is striving toward new frontiers of assigning the correct personalized drug(s) to a given patient. However, extensive tumor heterogeneity poses a major obstacle. Tumors of the same type often respond differently to therapy, due to patient-specific molecular aberrations and/or untargeted tumor subpopulations. It is frequently not possible to determine a priori which patients will respond to a certain therapy or how an efficient patient-specific combined therapy should be designed. Large-scale datasets have been growing at an accelerated pace and various technologies and analytical tools for single cell and bulk level analyses are being developed to extract significant individualized signals from such heterogeneous data. However, personalized therapies that dramatically alter the course of the disease remain scarce, and most tumors still respond poorly to medical care. In this review, the basic concepts of bulk and single cell approaches are discussed, as well as their emerging role in individualized designs of drug therapies, including the advantages and limitations of their applications in personalized medicine.

Single-cell analysis targeting the proteome

The existence of cellular heterogeneity and its central relevance to biological phenomena provides a strong rationale for a need for analytical methods that enable analysis at the single-cell level. Analysis of the genome and transcriptome is possible at the single-cell level, but the comprehensive interrogation of the proteome with this level of resolution remains challenging. Single-cell protein analysis tools are advancing rapidly, however, and providing insights into collections of proteins with great relevance to cell and disease biology. Here, we review single-cell protein analysis technologies and assess their advantages and limitations. The emerging technologies presented have the potential to reveal new insights into tumour heterogeneity and therapeutic resistance, elucidate mechanisms of immune response and immunotherapy, and accelerate drug discovery.

Dissecting the role of crosstalk between glioblastoma subpopulations in tumor cell spreading

Glioblastoma (GBM) is a highly infiltrative brain cancer, which is thus difficult to operate. GBM cells frequently harbor Epidermal Growth Factor Receptor amplification (EGFRwt) and/or activating mutation (EGFRvIII), generating at least two different cellular subpopulations within the tumor. We examined the relationship between the diffusive architectures of GBM tumors and the paracrine interactions between those subpopulations. Our aim was to shed light on what drives GBM cells to reach large cell–cell distances, and whether this characteristic can be manipulated. We established a methodology that quantifies the infiltration abilities of cancer cells through computation of cell–cell separation distance distributions in 3D. We found that aggressive EGFRvIII cells modulate the migration and infiltrative properties of EGFRwt cells. EGFRvIII cells secrete HGF and IL6, leading to enhanced activity of Src protein in EGFRwt cells, and rendering EGFRwt cells higher velocity and augmented ability to spread. Src inhibitor, dasatinib, at low non-toxic concentrations, reduced the infiltrative properties of EGFRvIII/EGFRwt neurospheres. Furthermore, dasatinib treatment induced compact multicellular microstructure packing of EGFRvIII/EGFRwt cells, impairing their ability to spread. Prevention of cellular infiltration or induction of compact microstructures may assist the detection of GBM tumors and tumor remnants in the brains and improve their surgical removal.

Decoding cancer heterogeneity: studying patient-specific signaling signatures towards personalized cancer therapy

The past years have witnessed a rapid increase in the amount of large-scale tumor datasets. The challenge has now become to find a way to obtain useful information from these masses of data that will allow to determine which combination of FDA-approved drugs is best suited to treat the specific tumor. Various statistical analyses are being developed to extract significant signals from cancer datasets. However, tumors are still being assigned to pre-defined categories (breast luminal A, triple negative, etc.), conceptually contradicting the vast heterogeneity that is known to exist among tumors, and likely overlooking unique tumors that must be addressed and treated individually. We present herein an approach based on information theory that, rather than searches for what makes a tumor similar to other tumors, addresses tumors individually and unbiasedly, and impartially decodes the critical patient-specific molecular network reorganization in every tumor.

Methods: Using a large dataset obtained from ~3500 tumors of 11 types we decipher the altered protein network structure in each tumor, namely the patient-specific signaling signature. Each signature can harbor several altered protein subnetworks. We suggest that simultaneous targeting of central proteins from every altered subnetwork is essential to efficiently disturb the altered signaling in each tumor. We experimentally validate our ability to dissect sample-specific signaling signatures and to rationally design personalized drug combinations.

Results: We unraveled a surprisingly simple order that underlies the extreme apparent complexity of tumor tissues, demonstrating that only 17 altered protein subnetworks characterize ~3500 tumors of 11 types. Each tumor was described by a specific subset of 1-4 subnetworks out of 17, i.e. a tumor-specific altered signaling signature. We show that the majority of tumor-specific signaling signatures are extremely rare, and are shared by only 5 tumors or less, supporting a personalized, comprehensive study of tumors in order to design the optimal combination therapy for every patient. We validate the results by confirming that the processes identified in the 11 original cancer types characterize patients harboring a different cancer type as well. We show experimentally, using different cancer cell lines, that the individualized combination therapies predicted by us achieved higher rates of killing than the clinically prescribed treatments.

Conclusions: We present a new strategy to deal with the inter-tumor heterogeneity and to break down the high complexity of cancer systems into simple, easy to crack, patient-specific signaling signatures that guide the rational design of personalized drug therapies.

Challenges in Developing Protein Secretion Assays at a Single-Cell Level

In addition to direct physical interactions between/among cells, the secretion of humoral factors from living cells is a critical process for cell-cell communications. A well-known extracellular signaling event is mediated by immune cell cytokines/chemokines. Because cell-cell communication is crucial in immune cell sociology, protein secretion assays first attracted a broad range of immunology interests. Now that we have entered an era of systems biology, cell-cell interactions mediated by secreted molecules should be revisited to understand the dynamics and homeostasis of the cell society as a whole. Of more importance, recent advances in detection and microfluidics technologies enable us to monitor protein secretion in real time rather than as a snapshot from the past, which gives us an opportunity to more deeply understand the logic of mammalian cell sociology. This chapter reviews the recent progress in and future direction of protein secretion assays, particularly from a mammalian cell sociology viewpoint.

Profiling the origin, dynamics, and function of traction force in B cell activation

B lymphocytes use B cell receptors (BCRs) to recognize membrane-bound antigens to further initiate cell spreading and contraction responses during B cell activation. We combined traction force microscopy and live-cell imaging to profile the origin, dynamics, and function of traction force generation in these responses. We showed that B cell activation required the generation of 10 to 20 nN of traction force when encountering antigens presented by substrates with stiffness values from 0.5 to 1 kPa, which mimic the rigidity of antigen-presenting cells in vivo. Perturbation experiments revealed that F-actin remodeling and myosin- and dynein-mediated contractility contributed to traction force generation and B cell activation. Moreover, membrane-proximal BCR signaling molecules (including Lyn, Syk, Btk, PLC-γ2, BLNK, and Vav3) and adaptor molecules (Grb2, Cbl, and Dok-3) linking BCR microclusters and motor proteins were also required for the sustained generation of these traction forces. We found a positive correlation between the strength of the traction force and the mean fluorescence intensity of the BCR microclusters. Furthermore, we demonstrated that isotype-switched memory B cells expressing immunoglobulin G (IgG)-BCRs generated greater traction forces than did mature naïve B cells expressing IgM-BCRs during B cell activation. Last, we observed that primary B cells from patients with rheumatoid arthritis generated greater traction forces than did B cells from healthy donors in response to antigen stimulation. Together, these data delineate the origin, dynamics, and function of traction force during B cell activation.

Personalized disease signatures through information-theoretic compaction of big cancer data

Every individual cancer develops and grows in its own specific way, giving rise to a recognized need for the development of personalized cancer diagnostics. This suggested that the identification of patient-specific oncogene markers would be an effective diagnostics approach. However, tumors that are classified as similar according to the expression levels of certain oncogenes can eventually demonstrate divergent responses to treatment. This implies that the information gained from the identification of tumor-specific biomarkers is still not sufficient. We present a method to quantitatively transform heterogeneous big cancer data to patient-specific transcription networks. These networks characterize the unbalanced molecular processes that deviate the tissue from the normal state. We study a number of datasets spanning five different cancer types, aiming to capture the extensive interpatient heterogeneity that exists within a specific cancer type as well as between cancers of different origins. We show that a relatively small number of altered molecular processes suffices to accurately characterize over 500 tumors, showing extreme compaction of the data. Every patient is characterized by a small specific subset of unbalanced processes. We validate the result by verifying that the processes identified characterize other cancer patients as well. We show that different patients may display similar oncogene expression levels, albeit carrying biologically distinct tumors that harbor different sets of unbalanced molecular processes. Thus, tumors may be inaccurately classified and addressed as similar. These findings highlight the need to expand the notion of tumor-specific oncogenic biomarkers to patient-specific, comprehensive transcriptional networks for improved patient-tailored diagnostics.

Addressing the Challenge of Molecular Change: An Interim Report

Invited by the editorial committee of the Annual Review of Physical Chemistry to "contribute my autobiography," I present it here, as I understand the term. It is about my parents, my mentors, my coworkers, and my friends in learning and the scientific problems that we tried to address. Courtesy of the editorial assistance of Annual Reviews, some of the science is in the figure captions and sidebars. I am by no means done: I am currently trying to fuse the quantitative rigor of physical chemistry with systems biology while also dealing with a post-Born-Oppenheimer regime in electronic dynamics and am attempting to instruct molecules to perform advanced logic.

Systems for localized release to mimic paracrine cell communication in vitro

Paracrine cell communication plays a pivotal role for signal exchange between proximal cells in vivo. However, this localized, gradient type release of mediators at very low concentrations (pg/ml), relevant during physiological and pathological processes, is rarely reflected within in vitro approaches. This review gives an overview on state-of-the-art approaches, which transfer the paracrine cell-to-cell communication into in vitro cell culture model setups. The traditional methods like trans-well assays and more advanced microfluidic approaches are included. The review focusses on systems for localized release, mostly based on microparticles, which tightly mimic the paracrine interaction between single cells in 3D microenvironments. Approaches based on single microparticles, with the main focus on affinity-controlled storage and release of cytokines, are reviewed and their importance for understanding paracrine communication is highlighted. Various methods to study the cytokine release and their advantages and disadvantages are discussed. Basic principles of the release characteristics, like diffusion mechanisms, are quantitatively described, including the formation of resulting gradients around the local sources. In vitro cell experiments using such localized microparticle release systems in approaches to increase understanding of stem cell behavior within their niches and regulation of wound healing are highlighted as examples of successful localized release systems for mimicking paracrine cell communication.

Single-cell technologies for profiling T cells to enable monitoring of immunotherapies

Immunotherapy relies on the reinvigoration of immune system to combat diseases and has transformed the landscape of cancer treatments. Clinical trials using immune checkpoint inhibitors (ICI), and adoptive transfer of genetically modified T cells have demonstrated durable remissions in subsets of cancer patients. A comprehensive understanding of the polyfunctionality of T lymphocytes in ICI or adoptive cell transfer (ACT), at single-cell resolution, will quantify T-cell properties that are essential for therapeutic benefit. We briefly highlight several emerging integrated single-cell technologies focusing on the profiling of multiple properties/functionalities of T cells. We envision that these tools have the potential to provide valuable experimental and clinical insights on T-cell biology, and eventually pave the road for the discovery of surrogate T-cell biomarkers for immunotherapy.

Joule Heating-Induced Dispersion in Open Microfluidic Electrophoretic Cytometry

While protein electrophoresis conducted in capillaries and microchannels offers high-resolution separations, such formats can be cumbersome to parallelize for single-cell analysis. One approach for realizing large numbers of concurrent separations is open microfluidics (i.e., no microchannels). In an open microfluidic device adapted for single-cell electrophoresis, we perform 100s to 1000s of simultaneous separations of endogenous proteins. The microscope slide-sized device contains cells isolated in microwells located in a ∼40 μm polyacrylamide gel. The gel supports protein electrophoresis after concurrent in situ chemical lysis of each isolated cell. During electrophoresis, Joule (or resistive) heating degrades separation performance. Joule heating effects are expected to be acute in open microfluidic devices, where a single, high-conductivity buffer expedites the transition from cell lysis to protein electrophoresis. Here, we test three key assertions. First, Joule heating substantially impacts analytical sensitivity due to diffusive losses of protein out of the open microfluidic electrophoretic (EP) cytometry device. Second, increased analyte diffusivity due to autothermal runaway Joule heating is a dominant mechanism that reduces separation resolution in EP cytometry. Finally, buffer exchange reduces diffusive losses and band broadening, even when handling single-cell lysate protein concentrations in an open device. We develop numerical simulations of Joule heating-enhanced diffusion during electrophoresis and observe ∼50% protein loss out of the gel, which is reduced using the buffer exchange. Informed by analytical model predictions of separation resolution (with Joule heating), we empirically demonstrate nearly fully resolved separations of proteins with molecular mass differences of just 4 kDa or 12% (GAPDH, 36 kDa; PS6, 32 kDa) in each of 129 single cells. The attained separation performance with buffer exchange is relevant to detection of currently unmeasurable protein isoforms responsible for cancer progression.

Top-down models in biology: explanation and control of complex living systems above the molecular level

It is widely assumed in developmental biology and bioengineering that optimal understanding and control of complex living systems follows from models of molecular events. The success of reductionism has overshadowed attempts at top-down models and control policies in biological systems. However, other fields, including physics, engineering and neuroscience, have successfully used the explanations and models at higher levels of organization, including least-action principles in physics and control-theoretic models in computational neuroscience. Exploiting the dynamic regulation of pattern formation in embryogenesis and regeneration requires new approaches to understand how cells cooperate towards large-scale anatomical goal states. Here, we argue that top-down models of pattern homeostasis serve as proof of principle for extending the current paradigm beyond emergence and molecule-level rules. We define top-down control in a biological context, discuss the examples of how cognitive neuroscience and physics exploit these strategies, and illustrate areas in which they may offer significant advantages as complements to the mainstream paradigm. By targeting system controls at multiple levels of organization and demystifying goal-directed (cybernetic) processes, top-down strategies represent a roadmap for using the deep insights of other fields for transformative advances in regenerative medicine and systems bioengineering.

Single cell proteomics in biomedicine: High-dimensional data acquisition, visualization, and analysis

New insights on cellular heterogeneity in the last decade provoke the development of a variety of single cell omics tools at a lightning pace. The resultant high-dimensional single cell data generated by these tools require new theoretical approaches and analytical algorithms for effective visualization and interpretation. In this review, we briefly survey the state-of-the-art single cell proteomic tools with a particular focus on data acquisition and quantification, followed by an elaboration of a number of statistical and computational approaches developed to date for dissecting the high-dimensional single cell data. The underlying assumptions, unique features, and limitations of the analytical methods with the designated biological questions they seek to answer will be discussed. Particular attention will be given to those information theoretical approaches that are anchored in a set of first principles of physics and can yield detailed (and often surprising) predictions.

Solute-mediated interactions between active droplets

Concentration gradients play a critical role in embryogenesis, bacterial locomotion, as well as the motility of active particles. Particles develop concentration profiles around them by dissolution, adsorption, or the reactivity of surface species. These gradients change the surface energy of the particles, driving both their self-propulsion and governing their interactions. Here, we uncover a regime in which solute gradients mediate interactions between slowly dissolving droplets without causing autophoresis. This decoupling allows us to directly measure the steady-state, repulsive force, which scales with interparticle distance as F∼1/r^{2}. Our results show that the dissolution process is diffusion rather than reaction rate limited, and the theoretical model captures the dependence of the interactions on droplet size and solute concentration, using a single fit parameter, l=16±3nm, which corresponds to the length scale of a swollen micelle. Our results shed light on the out-of-equilibrium behavior of particles with surface reactivity.

Current Advances in Highly Multiplexed Antibody-Based Single-Cell Proteomic Measurements

Single-cell measurements have played a critical role in revealing the complex signaling dynamics and heterogeneity present in cells, but there is still much to learn. Measuring samples from bulk populations of cells often masks the information and dynamics present in subsets of cells. Common single-cell protein studies rely on fluorescent microscopy and flow cytometry but are limited in multiplexing ability owing to spectral overlap. Recently, technology advancements in single-cell proteomics have allowed highly multiplexed measurement of multiple parameters simultaneously by using barcoded microfluidic enzyme-linked immunosorbent assays and mass cytometry techniques. In this review, we will describe recent work around multiparameter single-cell protein measurements and critically analyze the techniques.

Microchip-based single-cell functional proteomics for biomedical applications

Cellular heterogeneity has been widely recognized but only recently have single cell tools become available that allow characterizing heterogeneity at the genomic and proteomic levels. We review the technological advances in microchip-based toolkits for single-cell functional proteomics. Each of these tools has distinct advantages and limitations, and a few have advanced toward being applied to address biological or clinical problems that traditional population-based methods fail to address. High-throughput single-cell proteomic assays generate high-dimensional data sets that contain new information and thus require developing new analytical frameworks to extract new biology. In this review article, we highlight a few biological and clinical applications in which microchip-based single-cell proteomic tools provide unique advantages. The examples include resolving functional heterogeneity and dynamics of immune cells, dissecting cell-cell interaction by creating a well-controlled on-chip microenvironment, capturing high-resolution snapshots of immune system functions in patients for better immunotherapy and elucidating phosphoprotein signaling networks in cancer cells for guiding effective molecularly targeted therapies.