Digital colloid-enhanced Raman spectroscopy by single-molecule counting.

Quantitative detection of various molecules at very low concentrations in complex mixtures has been the main objective in many fields of science and engineering, from the detection of cancer-causing mutagens and early disease markers to environmental pollutants and bioterror agents1-5. Moreover, technologies that can detect these analytes without external labels or modifications are extremely valuable and often preferred6. In this regard, surface-enhanced Raman spectroscopy can detect molecular species in complex mixtures on the basis only of their intrinsic and unique vibrational signatures7. However, the development of surface-enhanced Raman spectroscopy for this purpose has been challenging so far because of uncontrollable signal heterogeneity and poor reproducibility at low analyte concentrations8. Here, as a proof of concept, we show that, using digital (nano)colloid-enhanced Raman spectroscopy, reproducible quantification of a broad range of target molecules at very low concentrations can be routinely achieved with single-molecule counting, limited only by the Poisson noise of the measurement process. As metallic colloidal nanoparticles that enhance these vibrational signatures, including hydroxylamine-reduced-silver colloids, can be fabricated at large scale under routine conditions, we anticipate that digital (nano)colloid-enhanced Raman spectroscopy will become the technology of choice for the reliable and ultrasensitive detection of various analytes, including those of great importance for human health.

Expectation maximization based framework for joint localization and parameter estimation in single particle tracking from segmented images.

Single Particle Tracking (SPT) is a well known class of tools for studying the dynamics of biological macromolecules moving inside living cells. In this paper, we focus on the problem of localization and parameter estimation given a sequence of segmented images. In the standard paradigm, the location of the emitter inside each frame of a sequence of camera images is estimated using, for example, Gaussian fitting (GF), and these locations are linked to provide an estimate of the trajectory. Trajectories are then analyzed by using Mean Square Displacement (MSD) or Maximum Likelihood Estimation (MLE) techniques to determine motion parameters such as diffusion coefficients. However, the problems of localization and parameter estimation are clearly coupled. Motivated by this, we have created an Expectation Maximization (EM) based framework for simultaneous localization and parameter estimation. We demonstrate this framework through two representative methods, namely, Sequential Monte Carlo combined with Expectation Maximization (SMC-EM) and Unscented Kalman Filter combined with Expectation Maximization (U-EM). Using diffusion in two-dimensions as a prototypical example, we conduct quantitative investigations on localization and parameter estimation performance across a wide range of signal to background ratios and diffusion coefficients and compare our methods to the standard techniques based on GF-MSD/MLE. To demonstrate the flexibility of the EM based framework, we do comparisons using two different camera models, an ideal camera with Poisson distributed shot noise but no readout noise, and a camera with both shot noise and the pixel-dependent readout noise that is common to scientific complementary metal-oxide semiconductor (sCMOS) camera. Our results indicate our EM based methods outperform the standard techniques, especially at low signal levels. While U-EM and SMC-EM have similar accuracy, U-EM is significantly more computationally efficient, though the use of the Unscented Kalman Filter limits U-EM to lower diffusion rates.

Direct single-molecule imaging for diagnostic and blood screening assays.

Every year, over 100 million units of donated blood undergo mandatory screening for HIV, hepatitis B, hepatitis C, and syphilis worldwide. Often, donated blood is also screened for human T cell leukemia-lymphoma virus, Chagas, dengue, Babesia, cytomegalovirus, malaria, and other infections. Several billion diagnostic tests are performed annually around the world to measure more than 400 biomarkers for cardiac, cancer, infectious, and other diseases. Considering such volumes, every improvement in assay performance and/or throughput has a major impact. Here, we show that medically relevant assay sensitivities and specificities can be fundamentally improved by direct single-molecule imaging using regular epifluorescence microscopes. In current microparticle-based assays, an ensemble of bound signal-generating molecules is measured as a whole. By contrast, we acquire intensity profiles to identify and then count individual fluorescent complexes bound to targets on antibody-coated microparticles. This increases the signal-to-noise ratio and provides better discrimination over nonspecific effects. It brings the detection sensitivity down to the attomolar (10-18 M) for model assay systems and to the low femtomolar (10-16 M) for measuring analyte in human plasma. Transitioning from counting single-molecule peaks to averaging pixel intensities at higher analyte concentrations enables a continuous linear response from 10-18 to 10-5 M. Additionally, our assays are insensitive to microparticle number and volume variations during the binding reaction, eliminating the main source of uncertainties in standard assays. Altogether, these features allow for increased assay sensitivity, wide linear detection ranges, shorter incubation times, simpler assay protocols, and minimal reagent consumption.

Single-particle cryo-EM at atomic resolution.

The three-dimensional positions of atoms in protein molecules define their structure and their roles in biological processes. The more precisely atomic coordinates are determined, the more chemical information can be derived and the more mechanistic insights into protein function may be inferred. Electron cryo-microscopy (cryo-EM) single-particle analysis has yielded protein structures with increasing levels of detail in recent years1,2. However, it has proved difficult to obtain cryo-EM reconstructions with sufficient resolution to visualize individual atoms in proteins. Here we use a new electron source, energy filter and camera to obtain a 1.7 Å resolution cryo-EM reconstruction for a human membrane protein, the ß3 GABAA receptor homopentamer3. Such maps allow a detailed understanding of small-molecule coordination, visualization of solvent molecules and alternative conformations for multiple amino acids, and unambiguous building of ordered acidic side chains and glycans. Applied to mouse apoferritin, our strategy led to a 1.22 Å resolution reconstruction that offers a genuine atomic-resolution view of a protein molecule using single-particle cryo-EM. Moreover, the scattering potential from many hydrogen atoms can be visualized in difference maps, allowing a direct analysis of hydrogen-bonding networks. Our technological advances, combined with further approaches to accelerate data acquisition and improve sample quality, provide a route towards routine application of cryo-EM in high-throughput screening of small molecule modulators and structure-based drug discovery.

Quantification of mRNA Expression Using Single-Molecule Nanopore Sensing.

RNA quantification methods are broadly used in life science research and in clinical diagnostics. Currently, real-time reverse transcription polymerase chain reaction (RT-qPCR) is the most common analytical tool for RNA quantification. However, in cases of rare transcripts or inhibiting contaminants in the sample, an extensive amplification could bias the copy number estimation, leading to quantification errors and false diagnosis. Single-molecule techniques may bypass amplification but commonly rely on fluorescence detection and probe hybridization, which introduces noise and limits multiplexing. Here, we introduce reverse transcription quantitative nanopore sensing (RT-qNP), an RNA quantification method that involves synthesis and single-molecule detection of gene-specific cDNAs without the need for purification or amplification. RT-qNP allows us to accurately quantify the relative expression of metastasis-associated genes MACC1 and S100A4 in nonmetastasizing and metastasizing human cell lines, even at levels for which RT-qPCR quantification produces uncertain results. We further demonstrate the versatility of the method by adapting it to quantify severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA against a human reference gene. This internal reference circumvents the need for producing a calibration curve for each measurement, an imminent requirement in RT-qPCR experiments. In summary, we describe a general method to process complicated biological samples with minimal losses, adequate for direct nanopore sensing. Thus, harnessing the sensitivity of label-free single-molecule counting, RT-qNP can potentially detect minute expression levels of RNA biomarkers or viral infection in the early stages of disease and provide accurate amplification-free quantification.

Molecular Dynamics Flexible Fitting: All You Want to Know About Resolution Exchange.

In recent years, owing to the advances in instrumentation, cryo-EM has emerged as the go-to tool for obtaining high-resolution structures of biomolecular systems. However, building three-dimensional atomic structures of biomolecules from these high-resolution maps remains a concern for the traditional map-guided structure-determination schemes. Recently, we developed a computational tool, Resolution Exchange Molecular Dynamics Flexible Fitting (ReMDFF) to address this problem by re-refining a search model against a series of maps of progressively higher resolutions, which ends with the original experimental resolution (Wang et al., J Struct Biol 204(2):319-328, 2018). In this chapter, we present a step-by-step outline for preparing, executing, and analyzing ReMDFF refinements of simple proteins and multimeric complexes. The structure determination of carbon monoxide dehydrogenase and Mg2+-channel CorA are employed as case studies. All scripts are provided via GitHub (Vant, Resolution exchange molecular dynamics flexible fitting (ReMDFF) all you want to know about flexible fitting, 2019, https://github.com/jvant/ReMDFF_Singharoy_Group.git ).

Correcting Artifacts in Single Molecule Localization Microscopy Analysis Arising from Pixel Quantum Efficiency Differences in sCMOS Cameras.

Optimal analysis of single molecule localization microscopy (SMLM) data acquired with a scientific Complementary Metal-Oxide-Semiconductor (sCMOS) camera relies on statistical compensation for its pixel-dependent gain, offset and readout noise. In this work we show that it is also necessary to compensate for differences in the relative quantum efficiency (RQE) of each pixel. We found differences in RQE on the order of 4% in our tested sCMOS sensors. These differences were large enough to have a noticeable effect on analysis algorithm results, as seen both in simulations and biological imaging data. We discuss how the RQE differences manifest themselves in the analysis results and present the modifications to the Poisson maximum likelihood estimation (MLE) sCMOS analysis algorithm that are needed to correct for the RQE differences.

A Super-Clustering Approach for Fully Automated Single Particle Picking in Cryo-EM.

Structure determination of proteins and macromolecular complexes by single-particle cryo-electron microscopy (cryo-EM) is poised to revolutionize structural biology. An early challenging step in the cryo-EM pipeline is the detection and selection of particles from two-dimensional micrographs (particle picking). Most existing particle-picking methods require human intervention to deal with complex (irregular) particle shapes and extremely low signal-to-noise ratio (SNR) in cryo-EM images. Here, we design a fully automated super-clustering approach for single particle picking (SuperCryoEMPicker) in cryo-EM micrographs, which focuses on identifying, detecting, and picking particles of the complex and irregular shapes in micrographs with extremely low signal-to-noise ratio (SNR). Our method first applies advanced image processing procedures to improve the quality of the cryo-EM images. The binary mask image-highlighting protein particles are then generated from each individual cryo-EM image using the super-clustering (SP) method, which improves upon base clustering methods (i.e., k-means, fuzzy c-means (FCM), and intensity-based cluster (IBC) algorithm) via a super-pixel algorithm. SuperCryoEMPicker is tested and evaluated on micrographs of ß-galactosidase and 80S ribosomes, which are examples of cryo-EM data exhibiting complex and irregular particle shapes. The results show that the super-particle clustering method provides a more robust detection of particles than the base clustering methods, such as k-means, FCM, and IBC. SuperCryoEMPicker automatically and effectively identifies very complex particles from cryo-EM images of extremely low SNR. As a fully automated particle detection method, it has the potential to relieve researchers from laborious, manual particle-labeling work and therefore is a useful tool for cryo-EM protein structure determination.

Multiplex chromatin interactions with single-molecule precision.

The genomes of multicellular organisms are extensively folded into 3D chromosome territories within the nucleus1. Advanced 3D genome-mapping methods that combine proximity ligation and high-throughput sequencing (such as chromosome conformation capture, Hi-C)2, and chromatin immunoprecipitation techniques (such as chromatin interaction analysis by paired-end tag sequencing, ChIA-PET)3, have revealed topologically associating domains4 with frequent chromatin contacts, and have identified chromatin loops mediated by specific protein factors for insulation and regulation of transcription5-7. However, these methods rely on pairwise proximity ligation and reflect population-level views, and thus cannot reveal the detailed nature of chromatin interactions. Although single-cell Hi-C8 potentially overcomes this issue, this method may be limited by the sparsity of data that is inherent to current single-cell assays. Recent advances in microfluidics have opened opportunities for droplet-based genomic analysis9 but this approach has not yet been adapted for chromatin interaction analysis. Here we describe a strategy for multiplex chromatin-interaction analysis via droplet-based and barcode-linked sequencing, which we name ChIA-Drop. We demonstrate the robustness of ChIA-Drop in capturing complex chromatin interactions with single-molecule precision, which has not been possible using methods based on population-level pairwise contacts. By applying ChIA-Drop to Drosophila cells, we show that chromatin topological structures predominantly consist of multiplex chromatin interactions with high heterogeneity; ChIA-Drop also reveals promoter-centred multivalent interactions, which provide topological insights into transcription.

High-Resolution "Fleezers": Dual-Trap Optical Tweezers Combined with Single-Molecule Fluorescence Detection.

Recent advances in optical tweezers have greatly expanded their measurement capabilities. A new generation of hybrid instrument that combines nanomechanical manipulation with fluorescence detection-fluorescence optical tweezers, or "fleezers"-is providing a powerful approach to study complex macromolecular dynamics. Here, we describe a combined high-resolution optical trap/confocal fluorescence microscope that can simultaneously detect sub-nanometer displacements, sub-piconewton forces, and single-molecule fluorescence signals. The primary technical challenge to these hybrid instruments is how to combine both measurement modalities without sacrificing the sensitivity of either one. We present general design principles to overcome this challenge and provide detailed, step-by-step instructions to implement them in the construction and alignment of the instrument. Lastly, we present a set of protocols to perform a simple, proof-of-principle experiment that highlights the instrument capabilities.