A high-throughput all-optical laser-scanning imaging flow cytometer with biomolecular specificity and subcellular resolution

Dual-view transport of intensity phase imaging flow cytometry

In this work, we design multi-parameter phase imaging flow cytometry based on dual-view transport of intensity (MPFC), which integrates phase imaging and microfluidics to a microscope, to obtain single-shot quantitative phase imaging on cells flowing in the microfluidic channel. The MPFC system has been proven with simple configuration, accurate phase retrieval, high imaging contrast, and real-time imaging and has been successfully employed not only in imaging, recognizing, and analyzing the flowing cells even with high-flowing velocities but also in tracking cell motilities, including rotation and binary rotation. Current results suggest that our proposed MPFC provides an effective tool for imaging and analyzing cells in microfluidics and can be potentially used in both fundamental and clinical studies.

Generalized and scalable trajectory inference in single-cell omics data with VIA

Inferring cellular trajectories using a variety of omic data is a critical task in single-cell data science. However, accurate prediction of cell fates, and thereby biologically meaningful discovery, is challenged by the sheer size of single-cell data, the diversity of omic data types, and the complexity of their topologies. We present VIA, a scalable trajectory inference algorithm that overcomes these limitations by using lazy-teleporting random walks to accurately reconstruct complex cellular trajectories beyond tree-like pathways (e.g., cyclic or disconnected structures). We show that VIA robustly and efficiently unravels the fine-grained sub-trajectories in a 1.3-million-cell transcriptomic mouse atlas without losing the global connectivity at such a high cell count. We further apply VIA to discovering elusive lineages and less populous cell fates missed by other methods across a variety of data types, including single-cell proteomic, epigenomic, multi-omics datasets, and a new in-house single-cell morphological dataset.

High-speed laser-scanning biological microscopy using FACED

Laser scanning is used in advanced biological microscopy to deliver superior imaging contrast, resolution and sensitivity. However, it is challenging to scale up the scanning speed required for interrogating a large and heterogeneous population of biological specimens or capturing highly dynamic biological processes at high spatiotemporal resolution. Bypassing the speed limitation of traditional mechanical methods, free-space angular-chirp-enhanced delay (FACED) is an all-optical, passive and reconfigurable laser-scanning approach that has been successfully applied in different microscopy modalities at an ultrafast line-scan rate of 1-80 MHz. Optimal FACED imaging performance requires optimized experimental design and implementation to enable specific high-speed applications. In this protocol, we aim to disseminate information allowing FACED to be applied to a broader range of imaging modalities. We provide (i) a comprehensive guide and design specifications for the FACED hardware; (ii) step-by-step optical implementations of the FACED module including the key custom components; and (iii) the overall image acquisition and reconstruction pipeline. We illustrate two practical imaging configurations: multimodal FACED imaging flow cytometry (bright-field, fluorescence and second-harmonic generation) and kHz 2D two-photon fluorescence microscopy. Users with basic experience in optical microscope operation and software engineering should be able to complete the setup of the FACED imaging hardware and software in ~2-3 months.

Analysis of signal detection configurations in optical time-stretch imaging

Optical time-stretch (OTS) imaging is effective for observing ultra-fast dynamic events in real time by virtue of its capability of acquiring images with high spatial resolution at high speed. In different implementations of OTS imaging, different configurations of its signal detection, i.e. fiber-coupled and free-space detection schemes, are employed. In this research, we quantitatively analyze and compare the two detection configurations of OTS imaging in terms of sensitivity and image quality with the USAF-1951 resolution chart and diamond films, respectively, providing a valuable guidance for the system design of OTS imaging in diverse fields.

Fast intelligent cell phenotyping for high-throughput optofluidic time-stretch microscopy based on the XGBoost algorithm

SIGNIFICANCE

The use of optofluidic time-stretch flow cytometry enables extreme-throughput cell imaging but suffers from the difficulties of capturing and processing a large amount of data. As significant amounts of continuous image data are generated, the images require identification with high speed.

AIM

We present an intelligent cell phenotyping framework for high-throughput optofluidic time-stretch microscopy based on the XGBoost algorithm, which is able to classify obtained cell images rapidly and accurately. The applied image recognition consists of density-based spatial clustering of applications with noise outlier detection, histograms of oriented gradients combining gray histogram fused feature, and XGBoost classification.

APPROACH

We tested the ability of this framework against other previously proposed or commonly used algorithms to phenotype two groups of cell images. We quantified their performances with measures of classification ability and computational complexity based on AUC and test runtime. The tested cell image datasets were acquired from high-throughput imaging of over 20,000 drug-treated and untreated cells with an optofluidic time-stretch microscope.

RESULTS

The framework we built beats other methods with an accuracy of over 97% and a classification frequency of 3000  cells  /  s. In addition, we determined the optimal structure of training sets according to model performances under different training set components.

CONCLUSIONS

The proposed XGBoost-based framework acts as a promising solution to processing large flow image data. This work provides a foundation for future cell sorting and clinical practice of high-throughput imaging cytometers.

PARC: ultrafast and accurate clustering of phenotypic data of millions of single cells

MOTIVATION

New single-cell technologies continue to fuel the explosive growth in the scale of heterogeneous single-cell data. However, existing computational methods are inadequately scalable to large datasets and therefore cannot uncover the complex cellular heterogeneity.

RESULTS

We introduce a highly scalable graph-based clustering algorithm PARC-Phenotyping by Accelerated Refined Community-partitioning-for large-scale, high-dimensional single-cell data (>1 million cells). Using large single-cell flow and mass cytometry, RNA-seq and imaging-based biophysical data, we demonstrate that PARC consistently outperforms state-of-the-art clustering algorithms without subsampling of cells, including Phenograph, FlowSOM and Flock, in terms of both speed and ability to robustly detect rare cell populations. For example, PARC can cluster a single-cell dataset of 1.1 million cells within 13 min, compared with >2 h for the next fastest graph-clustering algorithm. Our work presents a scalable algorithm to cope with increasingly large-scale single-cell analysis.

AVAILABILITY AND IMPLEMENTATION

https://github.com/ShobiStassen/PARC.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Parallelized volumetric fluorescence microscopy with a reconfigurable coded incoherent light-sheet array

Parallelized fluorescence imaging has been a long-standing pursuit that can address the unmet need for a comprehensive three-dimensional (3D) visualization of dynamical biological processes with minimal photodamage. However, the available approaches are limited to incomplete parallelization in only two dimensions or sparse sampling in three dimensions. We hereby develop a novel fluorescence imaging approach, called coded light-sheet array microscopy (CLAM), which allows complete parallelized 3D imaging without mechanical scanning. Harnessing the concept of an "infinity mirror", CLAM generates a light-sheet array with controllable sheet density and degree of coherence. Thus, CLAM circumvents the common complications of multiple coherent light-sheet generation in terms of dedicated wavefront engineering and mechanical dithering/scanning. Moreover, the encoding of multiplexed optical sections in CLAM allows the synchronous capture of all sectioned images within the imaged volume. We demonstrate the utility of CLAM in different imaging scenarios, including a light-scattering medium, an optically cleared tissue, and microparticles in fluidic flow. CLAM can maximize the signal-to-noise ratio and the spatial duty cycle, and also provides a further reduction in photobleaching compared to the major scanning-based 3D imaging systems. The flexible implementation of CLAM regarding both hardware and software ensures compatibility with any light-sheet imaging modality and could thus be instrumental in a multitude of areas in biological research.

Lab-On-A-Chip Device for Yeast Cell Characterization in Low-Conductivity Media Combining Cytometry and Bio-Impedance

This paper proposes a simple approach to optimize the operating frequency band of a lab-on-a-chip based on bio-impedance cytometry for a single cell. It mainly concerns applications in low-conductivity media. Bio-impedance allows for the characterization of low cell concentration or single cells by providing an electrical signature. Thus, it may be necessary to perform impedance measurements up to several tens of megahertz in order to extract the internal cell signature. In the case of single cells, characterization is performed in a very small volume down to 1 pL. At the same time, measured impedances increase from tens of kilo-ohms for physiological liquids up to several mega-ohms for low conductivity media. This is, for example, the case for water analysis. At frequencies above hundreds of kilohertz, parasitic effects, such as coupling capacitances, can prevail over the impedance of the sample and completely short-circuit measurements. To optimize the sensor under these conditions, a complete model of a cytometry device was developed, including parasitic coupling capacitances of the sensor to take into account all the impedances. It appears that it is possible to increase the pass band by optimizing track geometries and placement without changing the sensing area. This assumption was obtained by measuring and comparing electrical properties of yeast cells in a low-conductivity medium (tap water). Decreased coupling capacitance by a factor higher than 10 was obtained compared with a previous non-optimized sensor, which allowed for the impedance measurement of all electrical properties of cells as small as yeast cells in a low-conductivity medium.

Microfluidic Based Optical Microscopes on Chip

Last decade's advancements in optofluidics allowed obtaining an ever increasing integration of different functionalities in lab on chip devices to culture, analyze, and manipulate single cells and entire biological specimens. Despite the importance of optical imaging for biological sample monitoring in microfluidics, imaging is traditionally achieved by placing microfluidics channels in standard bench-top optical microscopes. Recently, the development of either integrated optical elements or lensless imaging methods allowed optical imaging techniques to be implemented in lab on chip systems, thus increasing their automation, compactness, and portability. In this review, we discuss known solutions to implement microscopes on chip that exploit different optical methods such as bright-field, phase contrast, holographic, and fluorescence microscopy.