Quantitative live-cell imaging of secretion activity reveals dynamic immune responses.

Quantification of cytokine secretion has facilitated advances in the field of immunology, yet the dynamic and varied secretion profiles of individual cells, particularly those obtained from limited human samples, remain obscure. Herein, we introduce a technology for quantitative live-cell imaging of secretion activity (qLCI-S) that enables high-throughput and dual-color monitoring of secretion activity at the single-cell level over several days, followed by transcriptome analysis of individual cells based on their phenotype. The efficacy of qLCI-S was demonstrated by visualizing the characteristic temporal pattern of cytokine secretion of group 2 innate lymphoid cells, which constitute less than 0.01% of human peripheral blood mononuclear cells, and by revealing minor subpopulations with enhanced cytokine production. The underlying mechanism of this feature was linked to the gene expression of stimuli receptors. This technology paves the way for exploring gene expression signatures linked to the spatiotemporal dynamic nature of various secretory functions.

A Wettability Contrast SERS Droplet Assay for Multiplexed Analyte Detection.

Droplet assay platforms have emerged as a significant methodology, providing distinct advantages such as sample compartmentalization, high throughput, and minimal analyte consumption. However, inherent complexities, especially in multiplexed detection, remain a challenge. We demonstrate a novel strategy to fabricate a plasmonic droplet assay platform (PDAP) for multiplexed analyte detection, enabling surface-enhanced Raman spectroscopy (SERS). PDAP efficiently splits a microliter droplet into submicroliter to nanoliter droplets under gravity-driven flow by wettability contrast between two distinct regions. The desired hydrophobicity and adhesive contrast between the silicone oil-grafted nonadhesive hydrophilic zone with gold nanoparticles is attained through (3-aminopropyl) triethoxysilane (APTES) functionalization of gold nanoparticles (AuNPs) using a scotch-tape mask. The wettability contrast surface facilitates the splitting of aqueous droplets with various surface tensions (ranging from 39.08 to 72 mN/m) into ultralow volumes of nanoliters. The developed PDAP was used for the multiplexed detection of Rhodamine 6G (Rh6G) and Crystal Violet (CV) dyes. The limit of detection for 120 nL droplet using PDAP was found to be 134 pM and 10.1 nM for Rh6G and CV, respectively. These results align with those from previously reported platforms, highlighting the comparable sensitivity of the developed PDAP. We have also demonstrated the competence of PDAP by testing adulterant spiked milk and obtained very good sensitivity. Thus, PDAP has the potential to be used for the multiplexed screening of food adulterants.

Fluorescence-Encoded Time-Domain Coherent Raman Spectroscopy in the Visible Range.

Fluorescence-encoded vibrational spectroscopy has attracted increasing attention by virtue of its high sensitivity and high chemical specificity. We recently demonstrated fluorescence-encoded time-domain coherent Raman spectroscopy (FLETCHERS), which enables low-frequency vibrational spectroscopy of low-concentration fluorophores using near-infrared (800-900 nm) light excitation. However, the feasibility of this study was constrained by the scarcity of excitable molecules in the near-infrared range. Consequently, the broader applicability of FLETCHERS has not been investigated. Here we extend the capabilities of FLETCHERS into the visible range by employing a noncollinear optical parametric amplifier as a light source, significantly enhancing its versatility. Specifically, we use the method, which we refer to as visible FLETCHERS (vFLETCHERS), to individually acquire Raman spectra from five visible fluorophores that have absorption peaks in the 600-700 nm region. These results not only confirm the versatility of vFLETCHERS for a wide range of molecules but also allude to its widespread applicability in biological research through highly sensitive supermultiplexed imaging.

Enhanced CRISPR/Cas12a-based quantitative detection of nucleic acids using double emulsion droplets.

Pairing droplet microfluidics and CRISPR/Cas12a techniques creates a powerful solution for the detection and quantification of nucleic acids at the single-molecule level, due to its specificity, sensitivity, and simplicity. However, traditional water-in-oil (W/O) single emulsion (SE) droplets often present stability issues, affecting the accuracy and reproducibility of assay results. As an alternative, water-in-oil-in-water (W/O/W) double emulsion (DE) droplets offer superior stability and uniformity for droplet digital assays. Moreover, unlike SE droplets, DE droplets are compatible with commercially available flow cytometry instruments for high-throughput analysis. Despite these advantages, no study has demonstrated the use of DE droplets for CRISPR-based nucleic acid detection. In our study, we conducted a comparative analysis to assess the performance of SE and DE droplets in quantitative detection of human papillomavirus type 18 (HPV18) DNA based on CRISPR/Cas12a. We evaluated the stability of SEs and DEs by examining size variation, merging extent, and content interaction before and after incubation at different temperatures and time points. By integrating DE droplets with flow cytometry, we achieved high-throughput and high-accuracy CRISPR/Cas12a-based quantification of target HPV18 DNA. The DE platform, when paired with CRISPR/Cas12a and flow cytometry techniques, emerges as a reliable tool for absolute quantification of nucleic acid biomarkers.

Neuronal sensorimotor integration guiding salt concentration navigation in Caenorhabditis elegans.

Animals navigate their environment by manipulating their movements and adjusting their trajectory which requires a sophisticated integration of sensory data with their current motor status. Here, we utilize the nematode Caenorhabditis elegans to explore the neural mechanisms of processing the sensory and motor information for navigation. We developed a microfluidic device which allows animals to freely move their heads while receiving temporal NaCl stimuli. We found that C. elegans regulates neck bending direction in response to temporal NaCl concentration changes in a way which is consistent with a C. elegans' navigational strategy which regulates traveling direction toward preferred NaCl concentrations. Our analysis also revealed that the activity of a neck motor neuron is significantly correlated with neck bending and activated by the decrease in NaCl concentration in a phase-dependent manner. By combining the analysis of behavioral and neural response to NaCl stimuli and optogenetic perturbation experiments, we revealed that NaCl decrease during ventral bending activates the neck motor neuron which counteracts ipsilateral bending. Simulations further suggest that this phase-dependent response of neck motor neurons can facilitate curving toward preferred salt concentrations.

Manipulation of photosynthetic energy transfer by vibrational strong coupling.

Uncovering the mystery of efficient and directional energy transfer in photosynthetic organisms remains a critical challenge in quantum biology. Recent experimental evidence and quantum theory developments indicate the significance of quantum features of molecular vibrations in assisting photosynthetic energy transfer, which provides the possibility of manipulating the process by controlling molecular vibrations. Here, we propose and theoretically demonstrate efficient manipulation of photosynthetic energy transfer by using vibrational strong coupling between the vibrational state of a Fenna-Matthews-Olson (FMO) complex and the vacuum state of an optical cavity. Specifically, based on a full-quantum analytical model to describe the strong coupling effect between the optical cavity and molecular vibration, we realize efficient manipulation of energy transfer efficiency (from 58% to 92%) and energy transfer time (from 20 to 500 ps) in one branch of FMO complex by actively controlling the coupling strength and the quality factor of the optical cavity under both near-resonant and off-resonant conditions, respectively. Our work provides a practical scenario to manipulate photosynthetic energy transfer by externally interfering molecular vibrations via an optical cavity and a comprehensible conceptual framework for researching other similar systems.

Passive microfluidic devices for cell separation.

The separation of specific cell populations is instrumental in gaining insights into cellular processes, elucidating disease mechanisms, and advancing applications in tissue engineering, regenerative medicine, diagnostics, and cell therapies. Microfluidic methods for cell separation have propelled the field forward, benefitting from miniaturization, advanced fabrication technologies, a profound understanding of fluid dynamics governing particle separation mechanisms, and a surge in interdisciplinary investigations focused on diverse applications. Cell separation methodologies can be categorized according to their underlying separation mechanisms. Passive microfluidic separation systems rely on channel structures and fluidic rheology, obviating the necessity for external force fields to facilitate label-free cell separation. These passive approaches offer a compelling combination of cost-effectiveness and scalability when compared to active methods that depend on external fields to manipulate cells. This review delves into the extensive utilization of passive microfluidic techniques for cell separation, encompassing various strategies such as filtration, sedimentation, adhesion-based techniques, pinched flow fractionation (PFF), deterministic lateral displacement (DLD), inertial microfluidics, hydrophoresis, viscoelastic microfluidics, and hybrid microfluidics. Besides, the review provides an in-depth discussion concerning cell types, separation markers, and the commercialization of these technologies. Subsequently, it outlines the current challenges faced in the field and presents a forward-looking perspective on potential future developments. This work hopes to aid in facilitating the dissemination of knowledge in cell separation, guiding future research, and informing practical applications across diverse scientific disciplines.

Enhancing the sensitivity and stability of electrochemical aptamer-based sensors by AuNPs@MXene nanocomposite for continuous monitoring of biomarkers.

Electrochemical aptamer-based (E-AB) sensors offer exciting potential for real-time tracking of various biomarkers, such as proteins and small molecules, due to their exceptional selectivity and adaptability. However, most E-AB sensors rely on planar gold structures, which inherently limit their sensitivity and operational stability for continuous monitoring of biomarkers. Although gold nanostructures have recently enhanced E-AB sensor performance, no studies have explored the combination of gold nanostructure with other types of nanomaterials for continuous molecular monitoring. To fill this gap, we employed gold nanoparticles and MXene Ti3C2 (AuNPs@MXene), a versatile nanocomposite, in designing an E-AB sensor targeted at vascular endothelial growth factor (VEGF), a crucial human signaling protein. Remarkably, the AuNPs@MXene nanocomposite achieved over thirty-fold and half-fold increases in active surface area compared to bare and AuNPs-modified gold electrodes, respectively, significantly elevating the analytical capabilities of E-AB sensors during continuous operation. After a systematic optimization and characterization process, the newly developed E-AB sensor, powered by AuNPs@MXene nanocomposite, demonstrated both enhanced stability and heightened sensitivity. Overall, our findings open new avenues for the incorporation of nanocomposites in E-AB sensor design, enabling the creation of more sensitive and durable real-time monitoring systems.

Deciphering the Potential of Multidimensional Carbon Materials for Surface-Enhanced Raman Spectroscopy through Density Functional Theory.

Surface-enhanced Raman spectroscopy (SERS) is a potent analytical tool, particularly for molecular identification and structural analysis. Conventional metallic SERS substrates, however, suffer from low reproducibility and compatibility with biological molecules. Recently, metal-free SERS substrates based on chemical enhancement have emerged as a promising alternative with carbon-based materials offering excellent reproducibility and compatibility. Nevertheless, our understanding of carbon materials in SERS remains limited, which hinders their rational design. Here we systematically explore multidimensional carbon materials, including zero-dimensional fullerenes (C60), one-dimensional carbon nanotubes, two-dimensional graphene, and their B-, N-, and O-doped derivatives, for SERS applications. Using density functional theory, we elucidate the nonresonant polarizability-enhanced and resonant charge-transfer-based chemical enhancement mechanisms of these materials by evaluating their static/dynamic polarizability and electron excitation properties. This work provides a critical reference for the future design of carbon-based SERS substrates, opening a new avenue in this field.

Time-dependent cell-state selection identifies transiently expressed genes regulating ILC2 activation.

The decision of whether cells are activated or not is controlled through dynamic intracellular molecular networks. However, the low population of cells during the transition state of activation renders the analysis of the transcriptome of this state technically challenging. To address this issue, we have developed the Time-Dependent Cell-State Selection (TDCSS) technique, which employs live-cell imaging of secretion activity to detect an index of the transition state, followed by the simultaneous recovery of indexed cells for subsequent transcriptome analysis. In this study, we used the TDCSS technique to investigate the transition state of group 2 innate lymphoid cells (ILC2s) activation, which is indexed by the onset of interleukin (IL)-13 secretion. The TDCSS approach allowed us to identify time-dependent genes, including transiently induced genes (TIGs). Our findings of IL4 and MIR155HG as TIGs have shown a regulatory function in ILC2s activation.

Boosting the Brightness of Raman Tags Using Cyanostar Macrocycles.

Raman probes have received growing attention for their potential use in super-multiplex biological imaging and flow cytometry applications that cannot be achieved using fluorescent probes. However, obtaining strong Raman scattering signals from small Raman probes has posed a challenge that holds back their practical implementation. Here, we present new types of Raman-active nanoparticles (Rdots) that incorporate ionophore macrocycles, known as cyanostars, to act as ion-driven and structure-directing spacers to address this problem. These macrocycle-enhanced Rdots (MERdots) exhibit sharper and higher electronic absorption peaks than Rdots. When combined with resonant broadband time-domain Raman spectroscopy, these MERdots show a ∼3-fold increase in Raman intensity compared to conventional Rdots under the same particle concentration. Additionally, the detection limit on the concentration of MERdots is improved by a factor of 2.5 compared to that of Rdots and a factor of 430 compared to that of Raman dye molecules in solution. The compact size of MERdots (26 nm in diameter) and their increased Raman signal intensity, along with the broadband capabilities of time-domain resonant Raman spectroscopy, make them promising candidates for a wide range of biological applications.

Is AI essential? Examining the need for deep learning in image-activated sorting of Saccharomyces cerevisiae.

Artificial intelligence (AI) has become a focal point across a multitude of societal sectors, with science not being an exception. Particularly in the life sciences, imaging flow cytometry has increasingly integrated AI for automated management and categorization of extensive cell image data. However, the necessity of AI over traditional classification methods when extending imaging flow cytometry to include cell sorting remains uncertain, primarily due to the time constraints between image acquisition and sorting actuation. AI-enabled image-activated cell sorting (IACS) methods remain substantially limited, even as recent advancements in IACS have found success while largely relying on traditional feature gating strategies. Here we assess the necessity of AI for image classification in IACS by contrasting the performance of feature gating, classical machine learning (ML), and deep learning (DL) with convolutional neural networks (CNNs) in the differentiation of Saccharomyces cerevisiae mutant images. We show that classical ML could only yield a 2.8-fold enhancement in target enrichment capability, albeit at the cost of a 13.7-fold increase in processing time. Conversely, a CNN could offer an 11.0-fold improvement in enrichment capability at an 11.5-fold increase in processing time. We further executed IACS on mixed mutant populations and quantified target strain enrichment via downstream DNA sequencing to substantiate the applicability of DL for the proposed study. Our findings validate the feasibility and value of employing DL in IACS for morphology-based genetic screening of S. cerevisiae, encouraging its incorporation in future advancements of similar technologies.

Unlocking the secrets of the invisible world: incredible deep optical imaging through in-silico clearing.

In-silico clearing enables deep optical imaging of biological samples by correcting image blur caused by scattering and aberration. This breakthrough method offers researchers unprecedented insights into three-dimensional biological systems, with enormous potential for advancing biology and medicine to better understand living organisms and human health.

The marriage of coherent Raman scattering imaging and advanced computational tools.

Coherent Raman scattering microscopy can provide high-contrast tissue and single-cell images based on the inherent molecular vibrations of the sample. However, conventional techniques face a three-way trade-off between Raman spectral bandwidth, imaging speed, and image fidelity. Although currently challenging to address via optical design, this trade-off can be overcome via emerging computational tools such as compressive sensing and machine learning.

Place & Play SERS: sample collection and preparation-free surface-enhanced Raman spectroscopy.

The ability to perform sensitive, real-time, in situ, multiplex chemical analysis is indispensable for diverse applications such as human health monitoring, food safety testing, forensic analysis, environmental sensing, and homeland security. Surface-enhanced Raman spectroscopy (SERS) is an effective tool to offer the ability by virtue of its high sensitivity and rapid label-free signal detection as well as the availability of portable Raman spectrometers. Unfortunately, the practical utility of SERS is limited because it generally requires sample collection and preparation, namely, collecting a sample from an object of interest and placing the sample on top of a SERS substrate to perform a SERS measurement. In fact, not all analytes can satisfy this requirement because the sample collection and preparation process may be undesirable, laborious, difficult, dangerous, costly, or time-consuming. Here we introduce "Place & Play SERS" based on an ultrathin, flexible, stretchable, adhesive, biointegratable gold-deposited polyvinyl alcohol (PVA) nanomesh substrate that enables placing the substrate on top of an object of interest and performing a SERS measurement of the object by epi-excitation without the need for touching, destroying, and sampling it. Specifically, we characterized the sensitivity of the gold/PVA nanomesh substrate in the Place & Play SERS measurement scheme and then used the scheme to conduct SERS measurements of both wet and dry objects under nearly real-world conditions. To show the practical utility of Place & Play SERS, we demonstrated two examples of its application: food safety testing and forensic analysis. Our results firmly verified the new measurement scheme of SERS and are expected to extend the potential of SERS by opening up untapped applications of sensitive, real-time, in situ multiplex chemical analysis.

Color-scalable flow cytometry with Raman tags.

Flow cytometry is an indispensable tool in biology and medicine for counting and analyzing cells in large heterogeneous populations. It identifies multiple characteristics of every single cell, typically via fluorescent probes that specifically bind to target molecules on the cell surface or within the cell. However, flow cytometry has a critical limitation: the color barrier. The number of chemical traits that can be simultaneously resolved is typically limited to several due to the spectral overlap between fluorescence signals from different fluorescent probes. Here, we present color-scalable flow cytometry based on coherent Raman flow cytometry with Raman tags to break the color barrier. This is made possible by combining a broadband Fourier-transform coherent anti-Stokes Raman scattering (FT-CARS) flow cytometer, resonance-enhanced cyanine-based Raman tags, and Raman-active dots (Rdots). Specifically, we synthesized 20 cyanine-based Raman tags whose Raman spectra are linearly independent in the fingerprint region (400 to 1,600 cm-1). For highly sensitive detection, we produced Rdots composed of 12 different Raman tags in polymer nanoparticles whose detection limit was as low as 12 nM for a short FT-CARS signal integration time of 420 µs. We performed multiplex flow cytometry of MCF-7 breast cancer cells stained by 12 different Rdots with a high classification accuracy of 98%. Moreover, we demonstrated a large-scale time-course analysis of endocytosis via the multiplex Raman flow cytometer. Our method can theoretically achieve flow cytometry of live cells with >140 colors based on a single excitation laser and a single detector without increasing instrument size, cost, or complexity.

Real-time intelligent classification of COVID-19 and thrombosis via massive image-based analysis of platelet aggregates.

Microvascular thrombosis is a typical symptom of COVID-19 and shows similarities to thrombosis. Using a microfluidic imaging flow cytometer, we measured the blood of 181 COVID-19 samples and 101 non-COVID-19 thrombosis samples, resulting in a total of 6.3 million bright-field images. We trained a convolutional neural network to distinguish single platelets, platelet aggregates, and white blood cells and performed classical image analysis for each subpopulation individually. Based on derived single-cell features for each population, we trained machine learning models for classification between COVID-19 and non-COVID-19 thrombosis, resulting in a patient testing accuracy of 75%. This result indicates that platelet formation differs between COVID-19 and non-COVID-19 thrombosis. All analysis steps were optimized for efficiency and implemented in an easy-to-use plugin for the image viewer napari, allowing the entire analysis to be performed within seconds on mid-range computers, which could be used for real-time diagnosis.