Label-free Flow Cytometry Research Articles

Single-cell laser emitting cytometry for label-free nucleolus fingerprinting

The nucleolus, a recognized biomolecular condensate, serves as the hub for ribosome biogenesis within the cell nucleus. Its quantity and morphology are discernible indicators of cellular functional states. However, precise identification and quantification of nucleoli remain challenging without specific labeling, particularly for suspended cells, tissue-level analysis and high-throughput applications. Here we introduce a single-cell laser emitting cytometry (SLEC) for label-free nucleolus differentiation through light-matter interactions within a Fabry–Perot resonator. The separated gain medium enhances the threshold difference by 36-fold between nucleolus and its surroundings, enabling selective laser emissions at nucleolar area while maintaining lower-order mode. The laser emission image provides insights into structural inhomogeneity, temporal fluid-like dynamics, and pathological application. Lasing spectral fingerprint depicts the quantity and size of nucleoli within a single cell, showcasing the label-free flow cytometry for nucleolus. This approach holds promise for nucleolus-guided cell screening and drug evaluation, advancing the study of diseases such as cancer and neurodegenerative disorders.

A Label-Free Optical Flow Cytometry Based-Method for Rapid Assay of Disinfectants' Bactericidal Activity.

Selecting the appropriate disinfectant to control and prevent healthcare-associated infections (HAIs) is a challenging task for environmental health experts due to the large number of available disinfectant products. This study aimed to develop a label-free flow cytometry (FCM) method for the rapid evaluation of bactericidal activity and to compare its efficacy with that of standard qualitative/quantitative suspension tests. The bactericidal efficiency of eight commercial disinfectants containing quaternary ammonium compounds (QACs) was evaluated against four strains recommended by EN 13727 (Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus, Enterococcus hirae) and four multidrug-resistant pathogens. The proposed FCM protocol measures changes in scattered light and counts following disinfectant exposure, neutralization, and culture steps. Unlike other available FCM-based methods, this approach does not rely on autofluorescence measurements, impedance cytometry, or fluorescent dyes. The FCM scattered light signals revealed both decreased count rates and morphological changes after treatment with minimum inhibitory concentrations (MICs) and higher concentrations for all tested bacteria. The results from the FCM measurements showed excellent correlation with those from standard assays, providing a rapid tool for monitoring the susceptibility profile of clinical, multidrug-resistant pathogens to chemical disinfectants, which could support infection prevention and control procedures for healthcare environments. This label-free FCM protocol offers a novel and rapid tool for environmental health experts, aiding in the optimization of disinfectant selection for the prevention and control of HAIs.

Surrogate gradient learning in spiking networks trained on event-based cytometry dataset

Spiking neural networks (SNNs) are bio-inspired neural networks that - to an extent - mimic the workings of our brains. In a similar fashion, event-based vision sensors try to replicate a biological eye as closely as possible. In this work, we integrate both technologies for the purpose of classifying micro-particles in the context of label-free flow cytometry. We follow up on our previous work in which we used simple logistic regression with binary labels. Although this model was able to achieve an accuracy of over 98%, our goal is to utilize the system for a wider variety of cells, some of which may have less noticeable morphological variations. Therefore, a more advanced machine learning model like the SNNs discussed here would be required. This comes with the challenge of training such networks, since they typically suffer from vanishing gradients. We effectively apply the surrogate gradient method to overcome this issue achieving over 99% classification accuracy on test data for a four-class problem. Finally, rather than treating the neural network as a black box, we explore the dynamics inside the network and make use of that to enhance its accuracy and sparsity.

Single-cell scattering and auto-fluorescence-based fast antibiotic susceptibility testing for gram-negative and gram-positive bacteria.

In this study, we assess the scattering of light and auto-fluorescence from single bacterial cells to address the challenge of fast (<2 h), label-free phenotypic antimicrobial susceptibility testing (AST). Label-free flow cytometry is used for monitoring both the respiration-related auto-fluorescence in two different fluorescence channels corresponding to FAD and NADH, and the morphological and structural information contained in the light scattered by individual bacteria during incubation with or without antibiotic. Large multi-parameter data are analyzed using dimensionality reduction methods, based either on a combination of 2D binning and Principal Component Analysis, or with a one-class Support Vector Machine approach, with the objective to predict the Susceptible or Resistant phenotype of the strain. For the first time, both Escherichia coli (Gram-negative) and Staphylococcus epidermidis (Gram-positive) isolates were tested with a label-free approach, and, in the presence of two groups of bactericidal antibiotic molecules, aminoglycosides and beta-lactams. Our results support the feasibility of label-free AST in less than 2 h and suggest that single cell auto-fluorescence adds value to the Susceptible/Resistant phenotyping over single-cell scattering alone, in particular for the mecA+ Staphylococcus (i.e., resistant) strains treated with oxacillin.

Improving the Classification Accuracy in Label-Free Flow Cytometry Using Event-Based Vision and Simple Logistic Regression

Event-based cameras are novel bio-inspired vision sensors that do not follow the mechanism of traditional frame-based cameras. In the field of data acquisition, the replacement of CMOS cameras with event-based cameras has proved to enhance the accuracy of machine learning methods in situations where critical lighting conditions and rapid dynamics are paramount. In this paper, we investigate for the first time the use of extreme learning machines on data coming from event-based cameras in the context of flow cytometry. Except for the image sensor, the experimental setup is similar to a setup we used in (Lugnan et al., 2020) where we showed that a simple linear classifier can achieve around 10% error rate on background-subtracted cell frames. Here, we show that the the error rate of this simple imaging flow cytometer could be decreased to less than 2% just by making use of the capabilities of an event camera. Moreover, additional benefits like more sensitivity and efficient memory usage are gained. Finally, we suggest further possible improvements to the experimental setup used to record events from flowing micro-particles allowing for more accurate and stable cellx sorting.

Label-free flow cytometry of rare circulating tumor cell clusters in whole blood

Circulating tumor cell clusters (CTCCs) are rare cellular events found in the blood stream of metastatic tumor patients. Despite their scarcity, they represent an increased risk for metastasis. Label-free detection methods of these events remain primarily limited to in vitro microfluidic platforms. Here, we expand on the use of confocal backscatter and fluorescence flow cytometry (BSFC) for label-free detection of CTCCs in whole blood using machine learning for peak detection/classification. BSFC uses a custom-built flow cytometer with three excitation wavelengths (405 nm, 488 nm, and 633 nm) and five detectors to detect CTCCs in whole blood based on corresponding scattering and fluorescence signals. In this study, detection of CTCC-associated GFP fluorescence is used as the ground truth to assess the accuracy of endogenous back-scattered light-based CTCC detection in whole blood. Using a machine learning model for peak detection/classification, we demonstrated that the combined use of backscattered signals at the three wavelengths enable detection of ~ 93% of all CTCCs larger than two cells with a purity of > 82% and an overall accuracy of > 95%. The high level of performance established through BSFC and machine learning demonstrates the potential for label-free detection and monitoring of CTCCs in whole blood. Further developments of label-free BSFC to enhance throughput could lead to important applications in the isolation of CTCCs in whole blood with minimal disruption and ultimately their detection in vivo.

Application of machine vision-based label-free flow cytometry to analyze t cell products

Application of machine vision-based label-free flow cytometry to analyze t cell products

Application of machine learning-driven label-free flow cytometry to analyze T cell products

Background & Aim Flow cytometry is a useful technique for analyzing phenotypic characteristics of cells and is used for measuring purity of target cells in heterogeneous cell populations for clinical diagnosis, as well as for controlling quality of therapeutic cell products and monitoring them in blood samples. One obstacle for analyzing the cell products by flow cytometry is, however, that it requires multiple cell labeling using specific antibodies/reagents under GMP compliance, and the process is time-consuming and costly. Here we introduce label-free ghost cytometry (LFGC), a novel machine learning-driven label-free flow cytometry that analyzes cell morphology based on cell-specific waveform signals without image production. LFGC can become a simple, fast and cost-effective analysis method of cells. In the present study, we assessed the potential applicability of LFGC to analyze cell characteristics for the quality control of cell products. Methods, Results & Conclusion A supervised machine learning model was developed from the label-free GC waveform signals of cells that were labeled with markers of Annexin V plus PI. The T cells were then classified into live or dead cells by applying the trained model to the label-free GC waveforms without seeing the fluorescent labels. In a similar manner, we assessed the ability of LFGC to identify T cells in human PBMC by labeling them with CD3. Furthermore, we applied LFGC for discriminating in-vitro activated T cells from resting T cells by labeling them with CD25. Our data showed that LFGC can accurately classify the live and dead T cells with area under the receiver operating characteristic curve (AUC) of 0.954. LFGC was also found to discriminate the CD3 positive T cells from PBMC with AUC of 0.967 and also the activated T cells from the resting ones with AUC of 0.987. These results support that LFGC can accurately predict the viability, purity and possibly functions of T cells without any labeling and could be thus beneficial for the quality control of cell products.

Multiplex Stimulated Raman Scattering Imaging Cytometry Reveals Lipid-Rich Protrusions in Cancer Cells under Stress Condition.

SummaryIn situ measurement of cellular metabolites is still a challenge in biology. Conventional methods, such as mass spectrometry or fluorescence microscopy, would either destroy the sample or introduce strong perturbations to target molecules. Here, we present multiplex stimulated Raman scattering (SRS) imaging cytometry as a label-free single-cell analysis platform with chemical specificity and high-throughput capabilities. Using SRS imaging cytometry, we studied the metabolic responses of human pancreatic cancer cells under stress by starvation and chemotherapeutic drug treatments. We unveiled protrusions containing lipid droplets as a metabolic marker for stress-resistant cancer cells. Furthermore, by spectroscopic SRS mapping, we unveiled that triglyceride in lipid droplets are used for local energy production through lipolysis, autophagy, and β-oxidation. Our findings demonstrate the potential of targeting lipid metabolism for selective treatment of stress-resistant cancers. Collectively, these results highlight SRS imaging cytometry as a powerful label-free tool for biological discoveries with a high-throughput, high-content capacity.

Autofluorescence is a Reliable in vitro Marker of Cellular Senescence in Human Mesenchymal Stromal Cells

Mesenchymal stromal cells (MSC) are used in cell therapies, however cellular senescence increases heterogeneity of cell populations and leads to uncertainty in therapies’ outcomes. The determination of cellular senescence is time consuming and logistically intensive. Here, we propose the use of endogenous autofluorescence as real-time quantification of cellular senescence in human MSC, based on label-free flow cytometry analysis. We correlated cell autofluorescence to senescence using senescence-associated beta-galactosidase assay (SA-β-Gal) with chromogenic (X-GAL) and fluorescent (C12FDG) substrates, gene expression of senescence markers (such as p16INK4A, p18INK4C, CCND2 and CDCA7) and telomere length. Autofluorescence was further correlated to MSC differentiation assays (adipogenesis, chondrogenesis and osteogenesis), MSC stemness markers (CD90/CD106) and cytokine secretion (IL-6 and MCP-1). Increased cell autofluorescence significantly correlated with increased SA-β-Gal signal (both X-GAL and C12FDG substrates), cell volume and cell granularity, IL-6/MCP-1 secretion and with increased p16INK4A and CCND2 gene expression. Increased cell autofluorescence was negatively associated with the expression of the CD90/CD106 markers, osteogenic and chondrogenic differentiation potentials and p18INK4C and CDCA7 gene expression. Cell autofluorescence correlated neither with telomere length nor with adipogenic differentiation potential. We conclude that autofluorescence can be used as fast and non-invasive senescence assay for comparing MSC populations under controlled culture conditions.

High-throughput label-free flow cytometry based on matched-filter compressive imaging.

We present a fast label-free computational flow cytometer based on a strategy of compressive imaging. Scattered light from flowing objects is sub-divided into user-defined basis patterns by a deformable mirror and routed to different detectors associated with each pattern. The patterns can be optimized to be matched to the object features of interest, thus facilitating object identification and separation. Compared to conventional scanning flow cytometers, our technique provides increased information capacity without sacrificing flow velocity. Unique features of our matched-filter strategy are that it can simultaneously probe multiple objects throughout large fields of view with long depths of field. In our proof-of-concept demonstrations, we achieve throughputs of over 10,000 particles/s, working at flow velocities of over 1m/s.

Electrical impedance microflow cytometry with oxygen control for detection of sickle cells

Polymerization of intracellular sickle hemoglobin induced by low oxygen tension has been recognized as a primary determinant of the pathophysiologic manifestations in sickle cell disease. Existing flow cytometry techniques for detection of sickle cells are typically based on fluorescence markers or cellular morphological analysis. Using microfluidics and electrical impedance spectroscopy, we develop a new, label-free flow cytometry for non-invasive measurement of single cells under controlled oxygen level. We demonstrate the capability of this new technique by determining the electrical impedance differential of normal red blood cells obtained from a healthy donor and sickle cells obtained from three sickle cell patients, under normoxic and hypoxic conditions and at three different electrical frequencies, 156kHz, 500kHz and 3MHz. Under normoxia, normal cells and sickle cells can be separated completely using electrical impedance at 156kHz and 500kHz but not at 3MHz. Sickle cells, intra-patient and inter-patient show significantly different electrical impedance between normoxia and hypoxia at all three frequencies. This study shows a proof of concept that electrical impedance signal can be used as an indicator of the disease state of a red blood cell as well as the cell sickling events in sickle cell disease. Electrical impedance-based microflow cytometry with oxygen control is a new method that can be potentially used for sickle cell disease diagnosis and monitoring.

Label-free flow cytometry using multiplex coherent anti-Stokes Raman scattering (MCARS) for the analysis of biological specimens

We present the first demonstration, to our knowledge, of a label-free flow cytometer for the analysis of biological specimens using multiplex coherent anti-Stokes Raman scattering (MCARS) and elastic scatter measurements. The MCARS system probes the Raman vibrational energy levels and the elastic scatter provides morphological information. We demonstrate these capabilities by probing a culture of Saccharomyces cerevisiae at 100 spectra/s and constructing a background-free Raman reconstruction using a Kramers-Kronig relation. A theoretical analysis shows that this system could operate at speeds of 10 kHz with appropriate hardware; thus facilitating integration into commercial flow cytometers or use as a high-speed, stand-alone device.

Light scattering and morphology of the lymphocyte as applied to flow cytometry for distinguishing healthy and infected individuals

A simple optical model of single lymphocytes with smooth and nonsmooth surfaces has been developed for healthy and infected individuals. The model can be used for rapid (in the real-time scale) solution of the inverse light-scattering problem on the basis of optical data measured by label-free flow cytometry. Light scattering patterns have been calculated for the model developed. It has been shown that the smooth and nonsmooth cells can be resolved using the intensities of the sideward- and backward-scattered light. We have found by calculations and validated by the flow cytometer experiments that intensity distributions for the cells of lymphocyte populations can be used as a preliminary signatures of some virus infections. Potential biomedical applications of the findings for label-free flow cytometry detection of individuals infected with viruses of hepatitis B or C and some others viruses are presented.

Multiplex coherent anti-Stokes Raman scattering (MCARS) for chemically sensitive, label-free flow cytometry

Flow cytometry is an ever-advancing high-throughput multivariate analysis tool that natively provides size and morphological information. To obtain molecular information, however, typically requires the addition of fluorophores, which are limited by spectral overlap, nonspecific binding, available conjugation chemistries, and cellular toxicity. A complementary or alternative, label-free approach to molecular information is through multiplex coherent anti-Stokes Raman scattering (MCARS), which is a coherent, nonlinear optical method that provides a wealth of molecular information by probing the Raman energies within a molecule. In this work, we demonstrate the unique capability of our MCARS flow cytometer to distinguish flowing particles and discuss system performance capabilities and possibilities.

Elastic Light Scattering from Single Cells: Orientational Dynamics in Optical Trap

Light-scattering diagrams (phase functions) from single living cells and beads suspended in an optical trap were recorded with 30-ms time resolution. The intensity of the scattered light was recorded over an angular range of 0.5–179.5° using an optical setup based on an elliptical mirror and rotating aperture. Experiments revealed that light-scattering diagrams from biological cells exhibit significant and complex time dependence. We have attributed this dependence to the cell's orientational dynamics within the trap. We have also used experimentally measured phase function information to calculate the time dependence of the optical radiation pressure force on the trapped particle and show how it changes depending on the orientation of the particle. Relevance of these experiments to potential improvement in the sensitivity of label-free flow cytometry is discussed.