Dispersion-free inertial focusing (DIF) for high-yield polydisperse micro-particle filtration and analysis.

Inertial focusing excels at the precise spatial ordering and separation of microparticles by size within fluid flows. However, this advantage, resulting from its inherent size-dependent dispersion, could turn into a drawback that challenges applications requiring consistent and uniform positioning of polydisperse particles, such as microfiltration and flow cytometry. To overcome this fundamental challenge, we introduce Dispersion-Free Inertial Focusing (DIF). This new method minimizes particle size-dependent dispersion while maintaining the high throughput and precision of standard inertial focusing, even in a highly polydisperse scenario. We demonstrate a rule-of-thumb principle to reinvent an inertial focusing system and achieve an efficient focusing of particles ranging from 6 to 30 µm in diameter onto a single plane with less than 3 µm variance and over 95% focusing efficiency at highly scalable throughput (2.4-30 mL h-1) - a stark contrast to existing technologies that struggle with polydispersity. We demonstrated that DIF could be applied in a broad range of applications, particularly enabling high-yield continuous microparticle filtration and large-scale high-resolution single-cell morphological analysis of heterogeneous cell populations. This new technique is also readily compatible with the existing inertial microfluidic design and thus could unleash more diverse systems and applications.

Information-Distilled Generative Label-Free Morphological Profiling Encodes Cellular Heterogeneity.

Image-based cytometry faces challenges due to technical variations arising from different experimental batches and conditions, such as differences in instrument configurations or image acquisition protocols, impeding genuine biological interpretation of cell morphology. Existing solutions, often necessitating extensive pre-existing data knowledge or control samples across batches, have proved limited, especially with complex cell image data. To overcome this, "Cyto-Morphology Adversarial Distillation" (CytoMAD), a self-supervised multi-task learning strategy that distills biologically relevant cellular morphological information from batch variations, is introduced to enable integrated analysis across multiple data batches without complex data assumptions or extensive manual annotation. Unique to CytoMAD is its "morphology distillation", symbiotically paired with deep-learning image-contrast translation-offering additional interpretable insights into label-free cell morphology. The versatile efficacy of CytoMAD is demonstrated in augmenting the power of biophysical imaging cytometry. It allows integrated label-free classification of human lung cancer cell types and accurately recapitulates their progressive drug responses, even when trained without the drug concentration information. CytoMAD  also allows joint analysis of tumor biophysical cellular heterogeneity, linked to epithelial-mesenchymal plasticity, that standard fluorescence markers overlook. CytoMAD can substantiate the wide adoption of biophysical cytometry for cost-effective diagnosis and screening.

Morphological profiling by high-throughput single-cell biophysical fractometry.

Complex and irregular cell architecture is known to statistically exhibit fractal geometry, i.e., a pattern resembles a smaller part of itself. Although fractal variations in cells are proven to be closely associated with the disease-related phenotypes that are otherwise obscured in the standard cell-based assays, fractal analysis with single-cell precision remains largely unexplored. To close this gap, here we develop an image-based approach that quantifies a multitude of single-cell biophysical fractal-related properties at subcellular resolution. Taking together with its high-throughput single-cell imaging performance (~10,000 cells/sec), this technique, termed single-cell biophysical fractometry, offers sufficient statistical power for delineating the cellular heterogeneity, in the context of lung-cancer cell subtype classification, drug response assays and cell-cycle progression tracking. Further correlative fractal analysis shows that single-cell biophysical fractometry can enrich the standard morphological profiling depth and spearhead systematic fractal analysis of how cell morphology encodes cellular health and pathological conditions.

Optofluidic imaging meets deep learning: from merging to emerging.

Propelled by the striking advances in optical microscopy and deep learning (DL), the role of imaging in lab-on-a-chip has dramatically been transformed from a silo inspection tool to a quantitative "smart" engine. A suite of advanced optical microscopes now enables imaging over a range of spatial scales (from molecules to organisms) and temporal window (from microseconds to hours). On the other hand, the staggering diversity of DL algorithms has revolutionized image processing and analysis at the scale and complexity that were once inconceivable. Recognizing these exciting but overwhelming developments, we provide a timely review of their latest trends in the context of lab-on-a-chip imaging, or coined optofluidic imaging. More importantly, here we discuss the strengths and caveats of how to adopt, reinvent, and integrate these imaging techniques and DL algorithms in order to tailor different lab-on-a-chip applications. In particular, we highlight three areas where the latest advances in lab-on-a-chip imaging and DL can form unique synergisms: image formation, image analytics and intelligent image-guided autonomous lab-on-a-chip. Despite the on-going challenges, we anticipate that they will represent the next frontiers in lab-on-a-chip imaging that will spearhead new capabilities in advancing analytical chemistry research, accelerating biological discovery, and empowering new intelligent clinical applications.

Low-Latency In Situ Image Analytics With FPGA-Based Quantized Convolutional Neural Network.

Real-time in situ image analytics impose stringent latency requirements on intelligent neural network inference operations. While conventional software-based implementations on the graphic processing unit (GPU)-accelerated platforms are flexible and have achieved very high inference throughput, they are not suitable for latency-sensitive applications where real-time feedback is needed. Here, we demonstrate that high-performance reconfigurable computing platforms based on field-programmable gate array (FPGA) processing can successfully bridge the gap between low-level hardware processing and high-level intelligent image analytics algorithm deployment within a unified system. The proposed design performs inference operations on a stream of individual images as they are produced and has a deeply pipelined hardware design that allows all layers of a quantized convolutional neural network (QCNN) to compute concurrently with partial image inputs. Using the case of label-free classification of human peripheral blood mononuclear cell (PBMC) subtypes as a proof-of-concept illustration, our system achieves an ultralow classification latency of 34.2 [Formula: see text] with over 95% end-to-end accuracy by using a QCNN, while the cells are imaged at throughput exceeding 29 200 cells/s. Our QCNN design is modular and is readily adaptable to other QCNNs with different latency and resource requirements.

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.

Toward Deep Biophysical Cytometry: Prospects and Challenges.

The biophysical properties of cells reflect their identities, underpin their homeostatic state in health, and define the pathogenesis of disease. Recent leapfrogging advances in biophysical cytometry now give access to this information, which is obscured in molecular assays, with a discriminative power that was once inconceivable. However, biophysical cytometry should go 'deeper' in terms of exploiting the information-rich cellular biophysical content, generating a molecular knowledge base of cellular biophysical properties, and standardizing the protocols for wider dissemination. Overcoming these barriers, which requires concurrent innovations in microfluidics, optical imaging, and computer vision, could unleash the enormous potential of biophysical cytometry not only for gaining a new mechanistic understanding of biological systems but also for identifying new cost-effective biomarkers of disease.

Deep-learning-assisted biophysical imaging cytometry at massive throughput delineates cell population heterogeneity.

The association of the intrinsic optical and biophysical properties of cells to homeostasis and pathogenesis has long been acknowledged. Defining these label-free cellular features obviates the need for costly and time-consuming labelling protocols that perturb the living cells. However, wide-ranging applicability of such label-free cell-based assays requires sufficient throughput, statistical power and sensitivity that are unattainable with current technologies. To close this gap, we present a large-scale, integrative imaging flow cytometry platform and strategy that allows hierarchical analysis of intrinsic morphological descriptors of single-cell optical and mass density within a population of millions of cells. The optofluidic cytometry system also enables the synchronous single-cell acquisition of and correlation with fluorescently labeled biochemical markers. Combined with deep neural network and transfer learning, this massive single-cell profiling strategy demonstrates the label-free power to delineate the biophysical signatures of the cancer subtypes, to detect rare populations of cells in the heterogeneous samples (10-5), and to assess the efficacy of targeted therapeutics. This technique could spearhead the development of optofluidic imaging cell-based assays that stratify the underlying physiological and pathological processes based on the information-rich biophysical cellular phenotypes.

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.

Quantitative Phase Imaging Flow Cytometry for Ultra-Large-Scale Single-Cell Biophysical Phenotyping.

Cellular biophysical properties are the effective label-free phenotypes indicative of differences in cell types, states, and functions. However, current biophysical phenotyping methods largely lack the throughput and specificity required in the majority of cell-based assays that involve large-scale single-cell characterization for inquiring the inherently complex heterogeneity in many biological systems. Further confounded by the lack of reported robust reproducibility and quality control, widespread adoption of single-cell biophysical phenotyping in mainstream cytometry remains elusive. To address this challenge, here we present a label-free imaging flow cytometer built upon a recently developed ultrafast quantitative phase imaging (QPI) technique, coined multi-ATOM, that enables label-free single-cell QPI, from which a multitude of subcellularly resolvable biophysical phenotypes can be parametrized, at an experimentally recorded throughput of >10,000 cells/s-a capability that is otherwise inaccessible in current QPI. With the aim to translate multi-ATOM into mainstream cytometry, we report robust system calibration and validation (from image acquisition to phenotyping reproducibility) and thus demonstrate its ability to establish high-dimensional single-cell biophysical phenotypic profiles at ultra-large-scale (>1,000,000 cells). Such a combination of throughput and content offers sufficiently high label-free statistical power to classify multiple human leukemic cell types at high accuracy (~92-97%). This system could substantiate the significance of high-throughput QPI flow cytometry in enabling next frontier in large-scale image-derived single-cell analysis applied in biological discovery and cost-effective clinical diagnostics. © 2019 International Society for Advancement of Cytometry.

Multi-ATOM: Ultrahigh-throughput single-cell quantitative phase imaging with subcellular resolution.

A growing body of evidence has substantiated the significance of quantitative phase imaging (QPI) in enabling cost-effective and label-free cellular assays, which provides useful insights into understanding the biophysical properties of cells and their roles in cellular functions. However, available QPI modalities are limited by the loss of imaging resolution at high throughput and thus run short of sufficient statistical power at the single-cell precision to define cell identities in a large and heterogeneous population of cells-hindering their utility in mainstream biomedicine and biology. Here we present a new QPI modality, coined multiplexed asymmetric-detection time-stretch optical microscopy (multi-ATOM) that captures and processes quantitative label-free single-cell images at ultrahigh throughput without compromising subcellular resolution. We show that multi-ATOM, based upon ultrafast phase-gradient encoding, outperforms state-of-the-art QPI in permitting robust phase retrieval at a QPI throughput of >10 000 cell/sec, bypassing the need for interferometry which inevitably compromises QPI quality under ultrafast operation. We employ multi-ATOM for large-scale, label-free, multivariate, cell-type classification (e.g. breast cancer subtypes, and leukemic cells vs peripheral blood mononuclear cells) at high accuracy (>94%). Our results suggest that multi-ATOM could empower new strategies in large-scale biophysical single-cell analysis with applications in biology and enriching disease diagnostics.

Microfluidic Imaging Flow Cytometry by Asymmetric-detection Time-stretch Optical Microscopy (ATOM).

Scaling the number of measurable parameters, which allows for multidimensional data analysis and thus higher-confidence statistical results, has been the main trend in the advanced development of flow cytometry. Notably, adding high-resolution imaging capabilities allows for the complex morphological analysis of cellular/sub-cellular structures. This is not possible with standard flow cytometers. However, it is valuable for advancing our knowledge of cellular functions and can benefit life science research, clinical diagnostics, and environmental monitoring. Incorporating imaging capabilities into flow cytometry compromises the assay throughput, primarily due to the limitations on speed and sensitivity in the camera technologies. To overcome this speed or throughput challenge facing imaging flow cytometry while preserving the image quality, asymmetric-detection time-stretch optical microscopy (ATOM) has been demonstrated to enable high-contrast, single-cell imaging with sub-cellular resolution, at an imaging throughput as high as 100,000 cells/s. Based on the imaging concept of conventional time-stretch imaging, which relies on all-optical image encoding and retrieval through the use of ultrafast broadband laser pulses, ATOM further advances imaging performance by enhancing the image contrast of unlabeled/unstained cells. This is achieved by accessing the phase-gradient information of the cells, which is spectrally encoded into single-shot broadband pulses. Hence, ATOM is particularly advantageous in high-throughput measurements of single-cell morphology and texture - information indicative of cell types, states, and even functions. Ultimately, this could become a powerful imaging flow cytometry platform for the biophysical phenotyping of cells, complementing the current state-of-the-art biochemical-marker-based cellular assay. This work describes a protocol to establish the key modules of an ATOM system (from optical frontend to data processing and visualization backend), as well as the workflow of imaging flow cytometry based on ATOM, using human cells and micro-algae as the examples.

High-throughput time-stretch imaging flow cytometry for multi-class classification of phytoplankton.

Time-stretch imaging has been regarded as an attractive technique for high-throughput imaging flow cytometry primarily owing to its real-time, continuous ultrafast operation. Nevertheless, two key challenges remain: (1) sufficiently high time-stretch image resolution and contrast is needed for visualizing sub-cellular complexity of single cells, and (2) the ability to unravel the heterogeneity and complexity of the highly diverse population of cells - a central problem of single-cell analysis in life sciences - is required. We here demonstrate an optofluidic time-stretch imaging flow cytometer that enables these two features, in the context of high-throughput multi-class (up to 14 classes) phytoplantkton screening and classification. Based on the comprehensive feature extraction and selection procedures, we show that the intracellular texture/morphology, which is revealed by high-resolution time-stretch imaging, plays a critical role of improving the accuracy of phytoplankton classification, as high as 94.7%, based on multi-class support vector machine (SVM). We also demonstrate that high-resolution time-stretch images, which allows exploitation of various feature domains, e.g. Fourier space, enables further sub-population identification - paving the way toward deeper learning and classification based on large-scale single-cell images. Not only applicable to biomedical diagnostic, this work is anticipated to find immediate applications in marine and biofuel research.

Acoustic rhinometry in pediatric sleep apnea.

PURPOSE: This prospective study aimed to evaluate the use of acoustic rhinometry (AR) in pediatric obstructive sleep apnea (OSA). METHODS: Children with clinically suspected OSA underwent AR measurements followed by attended overnight polysomnography. RESULTS: Of a total of 20 subjects (13 boys, seven girls), 15 (75%) had OSA, defined as apnea-hypopnea index (AHI) greater than or equal to five events per hour of sleep, and five had primary snoring (PS). The mean AHI was 16.79 vs. 1.96 events/h. Positional changes in airway measurement by AR were present in the OSA group, with an average decrease in nasal cavity volume from upright to supine position of 1.53 cm(3) (p = 0.027). These changes were predictive of sleep apnea (r (2) = 0.65, p = 0.035). CONCLUSIONS: This study demonstrates a marked difference between OSA and PS groups during AR measurements of the nasopharynx. Positional airway changes had been previously reported in adults with OSA and further evaluation of the airway function in pediatric OSA is warranted.

Rapid risk stratification for obstructive sleep apnea, based on snoring severity and body mass index.

OBJECTIVE: It is unclear whether all snoring patients require polysomnography, and there are no highly sensitive clinical predictors of sleep apnea. Our objective was to develop a simple clinical screening test for OSA in snoring patients. STUDY DESIGN: Prospective, IRB-approved study at a university sleep disorders center. SUBJECTS AND METHODS: In 211 patients undergoing polysomnography, snoring severity, Epworth sleepiness scale, body mass index, demographic, and sleep study data were collected. Receiver operating characteristic (ROC) analysis and Pearson correlation were used to develop a sensitive screening test for OSA. RESULTS: Snoring severity score (SSS) and BMI were the two most accurate predictors of OSA on the ROC curve. A bipartite threshold of SSS = 4 or BMI = 26 carried sensitivity of 97.4%, specificity of 40%, positive predictive value of 82.3%, and negative predictive value of 84.2% for moderate/severe OSA. Patients at high risk were those with BMI > or =32 (89% PPV) or SSS > or =7 (92% PPV). CONCLUSIONS: The statistic most predictive of OSA was snoring severity. Combining this with BMI yielded a highly sensitive screening test for moderate/severe OSA. This clinical assessment may be useful in risk-stratifying patients for polysomnography and therapy, facilitating deferred work-up in low-risk patients and expedited therapy in high-risk patients.

REM-associated nasal obstruction: a study with acoustic rhinometry during sleep.

OBJECTIVE: Obstructive sleep apnea events are more common in REM sleep, although there is no relationship between sleep phase and pharyngeal airway status. We studied the patency of the nasal airway during REM and non-REM sleep with the use of acoustic rhinometry. METHODS: Serial acoustic rhinometric assessment of nasal cross-sectional area was performed in 10 subjects, before sleep and during REM and non-REM sleep. All measurements were standardized to a decongested baseline with mean congestion factor (MCF). RESULTS: MCF in the seated position was 10.6% (+/-3.7) and increased with supine positioning to 16.2% (+/-2.3). In REM sleep, MCF was highest, at 22.3% (+/-1.7). In non-REM sleep, MCF was lowest, at 2.3% (+/-3.1). All interstage comparisons were statistically significant on repeated measures ANOVA (P < 0.05). CONCLUSION: REM sleep is characterized by significant nasal congestion; non-REM sleep, by profound decongestion. This phenomenon may be attributable to REM-dependent variation in cerebral blood flow that affects nasal congestion via the internal carotid system. REM-induced nasal congestion, an indirect effect of augmented cerebral perfusion, may contribute to the higher frequency of obstructive events in REM sleep.

Acoustic rhinometry predicts tolerance of nasal continuous positive airway pressure: a pilot study.

BACKGROUND: Nasal continuous positive airway pressure (nCPAP) is usually the first-line intervention for obstructive sleep apnea, but up to 50% of patients are unable to tolerate therapy because of discomfort-usually nasal complaints. No factors have been definitively correlated with nCPAP tolerance, although nasal cross-sectional area has been correlated with the level of CPAP pressure, and nasal surgery improves nCPAP compliance. This study examined the relationship between nasal cross-sectional area and nCPAP tolerance. METHODS: We performed acoustic rhinometry on 34 obstructive sleep apnea patients at the time of the initial sleep study. Patients titrated to nCPAP were interviewed 18 months after starting therapy to determine CPAP tolerance. Demographic, polysomnographic, and nasal cross-sectional area data were compared between CPAP-tolerant and -intolerant patients. RESULTS: Between 13 tolerant and 12 intolerant patients, there were no significant differences in age, gender, body mass index, CPAP level, respiratory disturbance index, or subjective nasal obstruction. Cross-sectional area at the inferior turbinate differed significantly between the two groups (p = 0.03). This remained significant after multivariate analysis for possibly confounding variables. A cross-sectional area cutoff of 0.6 cm2 at the head of the inferior turbinate carried a sensitivity of 75% and specificity of 77% for CPAP intolerance in this patient group. CONCLUSION: Nasal airway obstruction correlated with CPAP tolerance, supporting an important role for the nose in CPAP, and providing a physiological basis for improved CPAP compliance after nasal surgery. Objective nasal evaluation, but not the subjective report of nasal obstruction, may be helpful in the management of these patients.

Nasal obstruction and sleep-disordered breathing: a study using acoustic rhinometry.

BACKGROUND: The relationship between nasal airway function and sleep-disordered breathing (SDB) remains unclear. Although correction of nasal obstruction can significantly improve nighttime breathing in some patients, nasal obstruction may not play a role in all cases of SDB. An effective method of stratifying these patients is needed. Acoustic rhinometry (AR) is a reliable, noninvasive method of measuring the dimensions of the nasal airway. METHODS: In 44 patients, we performed acoustic rhinometric measurements of nasal airway cross-sectional area, followed by hospital-based polysomnography and nasal continuous positive airway pressure (nCPAP) level titration. We compared anatomic nasal obstruction to perceived nasal obstruction, as well as respiratory distress index and nCPAP titration level, using the Pearson correlation and multiple linear regression analysis within body mass index groups. RESULTS: Perceived nasal obstruction correlated significantly with objective anatomic obstruction as measured by AR (r = 0.45, p < 0.01). For certain subgroup analyses in patients with a body mass index below 25, AR measurements correlated significantly with both nCPAP titration pressure (r = 0.85, p < 0.01) and respiratory distress index (r = 0.67, p = 0,03). CONCLUSION: Nasal airway function may be a significant component of SDB in some patients, perhaps playing a larger role in patients who are not overweight. The best responders to nasal surgery for SDB may be nonoverweight patients with nasal obstruction. AR along with nasal examination may be helpful in the evaluation and treatment of the SDB patient.