Multimodal single-molecule microscopy with continuously controlled spectral resolution

Machine-Learning-Based Single-Molecule Quantification of Circulating MicroRNA Mixtures

MicroRNAs (miRs) are small noncoding RNAs that regulate gene expression and are emerging as powerful indicators of diseases. MiRs are secreted in blood plasma and thus may report on systemic aberrations at an early stage via liquid biopsy analysis. We present a method for multiplexed single-molecule detection and quantification of a selected panel of miRs. The proposed assay does not depend on sequencing, requires less than 1 mL of blood, and provides fast results by direct analysis of native, unamplified miRs. This is enabled by a novel combination of compact spectral imaging and a machine learning-based detection scheme that allows simultaneous multiplexed classification of multiple miR targets per sample. The proposed end-to-end pipeline is extremely time efficient and cost-effective. We benchmark our method with synthetic mixtures of three target miRs, showcasing the ability to quantify and distinguish subtle ratio changes between miR targets.

Pressure-controlled microfluidics for automated single-molecule sample preparation

Sample preparation is a crucial step in single-molecule experiments and involves passivating the microfluidic sample chamber, immobilizing the molecules, and setting experimental buffer conditions. The efficiency of the experiment depends on the quality and speed of sample preparation, which is often performed manually and relies on the experience of the experimenter. This can result in inefficient use of single-molecule samples and time, especially for high-throughput applications. To address this, a pressure-controlled microfluidic system is proposed to automate single-molecule sample preparation. The hardware is based on microfluidic components from ElveFlow and is designed to be cost-effective and adaptable to various microscopy applications. The system includes a reservoir pressure adapter and a reservoir holder designed for additive manufacturing. Two flow chamber designs Ibidi µ-slide and Grace Bio-Labs HybriWell chamber are characterized, and the flow characteristics of the liquid at different volume flow rates V˙ are simulated using CFD-simulations and compared to experimental and theoretical values. The goal of this work is to establish a straightforward and robust system for single-molecule sample preparation that can increase the efficiency of experiments and reduce the bottleneck of manual sample preparation, particularly for high-throughput applications.

Multipair Förster Resonance Energy Transfer via Spectrally Resolved Single-Molecule Detection

Förster resonance energy transfer (FRET) is a powerful tool for studying molecular interactions. Its use for studying interactions involving more than two molecules, however, has been limited by spectral crosstalk among the fluorophores. Here, we report multispectral FRET (msFRET) for imaging multiple pairs of interactions in parallel by spectrally resolving single fluorescent molecules. By using a dual (positional and spectral) channel and wide-field imaging configuration, fluorophores with emission maxima as close as 6-10 nm could be reliably distinguished. We demonstrate msFRET by continuously monitoring the hybridization dynamics among 2 × 2 pairs of DNA oligos in parallel using Cy3 and Cy3.5 as donors and Cy5 and Cy5.5 as acceptors. Aside from studying molecular interactions, msFRET may also find applications in probing fluorophore photophysics during FRET and in multiplexed superresolution imaging.

Live-cell fluorescence spectral imaging as a data science challenge

Live-cell fluorescence spectral imaging is an evolving modality of microscopy that uses specific properties of fluorophores, such as excitation or emission spectra, to detect multiple molecules and structures in intact cells. The main challenge of analyzing live-cell fluorescence spectral imaging data is the precise quantification of fluorescent molecules despite the weak signals and high noise found when imaging living cells under non-phototoxic conditions. Beyond the optimization of fluorophores and microscopy setups, quantifying multiple fluorophores requires algorithms that separate or unmix the contributions of the numerous fluorescent signals recorded at the single pixel level. This review aims to provide both the experimental scientist and the data analyst with a straightforward description of the evolution of spectral unmixing algorithms for fluorescence live-cell imaging. We show how the initial systems of linear equations used to determine the concentration of fluorophores in a pixel progressively evolved into matrix factorization, clustering, and deep learning approaches. We outline potential future trends on combining fluorescence spectral imaging with label-free detection methods, fluorescence lifetime imaging, and deep learning image analysis.

Imaging Dynamic Processes in Multiple Dimensions and Length Scales

Optical microscopy has become an invaluable tool for investigating complex samples. Over the years, many advances to optical microscopes have been made that have allowed us to uncover new insights into the samples studied. Dynamic changes in biological and chemical systems are of utmost importance to study. To probe these samples, multidimensional approaches have been developed to acquire a fuller understanding of the system of interest. These dimensions include the spatial information, such as the three-dimensional coordinates and orientation of the optical probes, and additional chemical and physical properties through combining microscopy with various spectroscopic techniques. In this review, we survey the field of multidimensional microscopy and provide an outlook on the field and challenges that may arise. Expected final online publication date for the Annual Review of Physical Chemistry, Volume 73 is April 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.