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Recent development of imaging flow cytometry (IFC) has enabled the measurements of single cells with high throughput, where fluorescent labels provide specificity for cellular diagnosis. The fluorescent labels may disturb the cell functions, and the requirements for high-throughput measurements limit the cell image quality. Here, we develop the high-content video flow cytometry (VFC) that measures unlabeled single cells with a rate of approximately 1000 cells per minute. For the obtained big data, the frame of interest (FOI) is automatically prepared by a digital cell filtering technique with machine learning. Cervical carcinoma cell lines (Caski, HeLa and C33-A cells) are differentiated with an accuracy of 91.5%, 90.5%, and 90.5% by deep learning in a three-way classification, respectively. The high-content VFC not only provides high-quality images of single cells with high throughput and rewinding, but also performs automatic digital cell filtering and label-free cell classification that may have clinical applications.

The in-flow detection of microparticles is of great importance in biomedical fields. Previous studies have shown that twodimensional (2D) light scattering technology has the capability to detect and differentiate unlabeled cells, while fluorescence imaging can be used as a specific cytological detection method. Here, we built a system to acquire simultaneously the 2D light scattering patterns and fluorescence images of single microparticles in flow. A 488 nm laser beam was reshaped into a light sheet by a cylindrical lens, which was used to excite the microparticles in a microfluidic tube. The 2D light scattering and fluorescence signals were collected by a 10x objective lens through a 505 nm dichroic beam splitter, and they were simultaneously acquired by dual complementary metal oxide semiconductor (CMOS) detectors. Our system acquired the 2D light scattering patterns and fluorescence images of single microparticles from a mixture (2 μm fluorescent particles, 2 μm and 3.87 μm non-fluorescent particles) in flow. Based on the 2D light scattering patterns and the fluorescence images, the number and the ratio of mixed microparticles can be determined. Moreover, the label-free microparticle size differentiation can also be achieved with the 2D light scattering patterns. The detection of microparticles in flow that combines label-free 2D light scattering technology and fluorescence imaging may find potential applications for single cell analysis.

Background: The high incidence and mortality rates of cervical cancer pose a serious threat to women's health. Traditional chemotherapy has inevitable drawbacks of nonspecific tumor targeting, high toxicity, and poor therapeutic efficiency. In order to overcome these shortcomings, a novel multifunctional magnetic nanoparticles drug delivery system with tumor targeting and magnetic resonance imaging was developed to achieve precise diagnosis and targeted tumor killing effects.Methods: Transmission electron microscopy, dynamic light scatting and ultraviolet methods were used to characterize the nanoparticles in vitro. Cell function tests were performed by scratch, transwell and flow cytometry assays. MTT was used to detect the toxicity of the nanoparticles. The motion trajectory, drug release and uptake studies were carried out in vitro. The in vivo pharmacokinetic and drug distribution studies were verified by high performance liquid chromatography methods. Attenuation of the MRI signal by the nanoparticles and their enhanced antitumor efficiency were examined in vivo in mouse cervical cancer models. Sequencing and proteomics were used to detect the key antitumor molecules of the nanoparticles.Results: Multifunctional magnetic nanoparticles coated with ferric oxide nanoparticles and doxorubicin hydrochloride (DOX-Fe3O4-PEG-PLA-NPs) was prepared successfully. No toxicity was detected of PEG-PLA-NP, however, the tumor killing effect was enhanced under the alternating magnetic field significantly. The drug-release study showed that the cumulative release rates of NP groups were much less than free DOX group, while the drug release rate increased under acidic condition. In addition, DOX-Fe3O4-PEG-PLA-NPs showed improved internalized into carcinoma cells under magnetic field significantly. In vivo studies demonstrated that the combined therapy under an alternating magnetic field displayed improved therapeutic effect when compared with individual therapies as documented by the delayed tumor growth, inhibition of metastasis, and prolonged survival. The in vitro and in vivo MRI results showed that the multifunctional magnetic nanomaterial had a better MRI signal reduction effect and a higher T2 relaxation rate.Conclusions: We developed an cervical cancer targeting nano-carrier drug delivery system successfully, which showed perfect excellent T2 contrast magnetic resonance imaging, chemotherapy-sensitizing, tumor targeting , and anti-tumor effect, thus have the potential to be a new theranostic strategy for ovarian cancer patients.

White blood cells are a significant part of immune system, which can prevent human body from infection and invasion of foreign invaders. The count and recognition of white blood cells plays an important role in modern clinical practice. There is an urgent need to modify the conventional methods (such as cytometry), which are time-consuming and labor-intensive for white blood cell counting. This paper describes a microfluidic cytometer for white blood cells integrated a three-dimensional hydrodynamic focusing system and on-chip optical components, which can realize single-cell fluid flow and single-cell detection. Through the experiment, the device achieves the hydrodynamic focusing of cell flow and the detection of side scatter and fluorescence. For classifying and counting of white blood cells, we further perform an experiment using blood samples and get fairly good results.

A new diagnosis approach for ovarian cancer (OC) based on spoof surface plasmon polaritons (SPPs) is proposed and experimentally demonstrated, which is label-free, damage-free, and sensitive. SPPs exhibit high electric field confinement and non-diffraction limit. Due to the resonance of split ring resonators (SRRs), the proposed device further enhances the localized field and thus achieves high sensitivity for biosensing. Normal ovarian, serous ovarian cancer, and ovarian clear cell carcinoma tissues were mounted on the SRRs for detection, and different frequency shifting of 90, 212, and 203 MHz was observed, respectively. This approach may also be suitable for diagnosis of other cancerous tissues.

We develop a single-mode fiber-based cytometer for the obtaining of two-dimensional (2D) light scattering patterns from static single cells. Anisotropy of the 2D light scattering patterns of single cells from ovarian cancer and normal cell lines is investigated by histograms of oriented gradients (HOG) method. By analyzing the HOG descriptors with support vector machine, an accuracy rate of 92.84% is achieved for the automatic classification of these two kinds of label-free cells. The 2D light scattering anisotropy cytometry combined with machine learning may provide a label-free, automatic method for screening of ovarian cancer cells, and other types of cells. © 2019 International Society for Advancement of Cytometry.

Small cell lung cancer (SCLC) needs to be classified from poorly differentiated lung adenocarcinoma (PDLAC) for appropriate treatment of lung cancer patients. Currently, the classification is achieved by experienced clinicians, radiologists and pathologists based on subjective and qualitative analysis of imaging, cytological and immunohistochemical (IHC) features. Label-free classification of lung cancer cell lines is developed here by using two-dimensional (2D) light scattering static cytometric technique. Measurements of scattered light at forward scattering (FSC) and side scattering (SSC) by using conventional cytometry show that SCLC cells are overlapped with PDLAC cells. However, our 2D light scattering static cytometer reveals remarkable differences between the 2D light scattering patterns of SCLC cell lines (H209 and H69) and PDLAC cell line (SK-LU-1). By adopting support vector machine (SVM) classifier with leave-one-out cross-validation (LOO-CV), SCLC and PDLAC cells are automatically classified with an accuracy of 99.87%. Our label-free 2D light scattering static cytometer may serve as a new, accurate, and easy-to-use method for the automatic classification of SCLC and PDLAC cells. © 2018 International Society for Advancement of Cytometry.

Despite the wide use of cytometry for white blood cell classification, the performance of traditional cytometers in point-of-care testing remains to be improved. Microfluidic techniques have been shown with considerable potentials in the development of portable devices. Here we present a prototype of microfluidic cytometer which integrates a three-dimensional hydrodynamic focusing system and an on-chip optical system to count and classify white blood cells. By adjusting the flow speed of sheath flow and sample flow, the blood cells can be horizontally and vertically focused in the center of microchannel. Optical fibers and on-chip microlens are embedded for the excitation and detection of single-cell. The microfluidic chip was validated by classifying white blood cells from clinical blood samples.

Cervical cancer is a major gynecological malignant tumor that threatens women's health. Current cytological methods have certain limitations for cervical cancer early screening. Light scattering patterns can reflect small differences in the internal structure of cells. In this study, we develop a light scattering pattern specific convolutional network (LSPS-net) based on deep learning algorithm and integrate it into a 2D light scattering static cytometry for automatic, label-free analysis of single cervical cells. An accuracy rate of 95.46% for the classification of normal cervical cells and cancerous ones (mixed C-33A and CaSki cells) is obtained. When applied for the subtyping of label-free cervical cell lines, we obtain an accuracy rate of 93.31% with our LSPS-net cytometric technique. Furthermore, the three-way classification of the above different types of cells has an overall accuracy rate of 90.90%, and comparisons with other feature descriptors and classification algorithms show the superiority of deep learning for automatic feature extraction. The LSPS-net static cytometry may potentially be used for cervical cancer early screening, which is rapid, automatic and label-free. This article is protected by copyright. All rights reserved.

Conventional flow cytometry has been used for leukemia characterization via fluorescence measurements. Here we measured the 2D light scattering patterns of label-free HL-60 cells (human acute myeloid leukemic cells) and K562 cells (human chronic myeloid leukemic cells) with a light-sheet illuminated flow cytometer. Approximately 70 light scattering patterns of leukemia cells were obtained in a one minute video taken by this cytometer operating at 50 frames per second. Local binary pattern (LBP) was used to extract features of the 2D light scattering images, which were then analyzed by the support vector machine (SVM) algorithm. An accuracy rate of 98.23% was obtained for the label-free classification of these two kinds of leukemia cells, with a specificity of 99.28% and a sensitivity of 97.22%. The combination of light-sheet flow cytometry with machine learning may be helpful for leukemia subtyping diagnosis in clinics.

Terahertz (THz) wave has significant potential in label-free biomedical diagnosis because of its fingerprint spectrum and noninvasive and nonionizing properties to living organisms. Here, a transmission continuous-wave THz spectroscopy system was used to measure 3 groups of human cancerous and normal ovarian tissues. Fresh tissue samples were made into paraffin-embedded tissue sections with a thickness of 1-2 mm, and the THz transmittance spectra of these samples were obtained in the 0.4-1.5 THz range. These results indicate that cancerous ovarian tissues have a higher THz absorption compared with normal ones. The absorption peaks at 0.588, 0.748 and 0.812 THz of normal ovarian tissue get weaker and even disappear in the transmittance line of serous ovarian cancer tissue, while the absorption peak at 1.396 THz get enhanced. Our preliminary results demonstrate the promise of continuous-wave THz spectroscopy technology in label-free ovarian cancer detection.

Two-dimensional (2D) light scattering has the capability for label-free single cell analysis. Recent development of flow cytometry has demonstrated the obtaining of high-content images. Here we demonstrate a flow cytometer for the obtaining of high-content 2D light scattering patterns of single cells. In our flow cytometer, single cells are flowing in a hydrodynamic focusing unit and their 2D light scattering patterns are recorded via a long working distance objective by using a high-speed complementary metal oxide semiconductor (CMOS) sensor. Big data of the 2D light scattering patterns from two types of cervical carcinoma cell lineage cells (HeLa and C33-A) are obtained with a rate of 60 frames per second. Deep learning is adopted for the classification of these two types of cells, and a high recognition accuracy is obtained. The results show that our high-content 2D light scattering flow cytometry together with deep learning can collect label-free single-cell information at high speed and has strong analytical capabilities, which may in future be used for early diagnosis of cervical carcinoma.

Background Women with congenital heart disease are considered at high risk for adverse events. Therefore, we aim to establish 2 prediction models for mothers and their offspring, which can predict the risk of adverse events occurred in pregnant women with congenital heart disease. Methods and Results A total of 318 pregnant women with congenital heart disease were included; 213 women were divided into the development cohort, and 105 women were divided into the validation cohort. Least absolute shrinkage and selection operator was used for predictor selection. After validation, multivariate logistic regression analysis was used to develop the model. Machine learning algorithms (support vector machine, random forest, AdaBoost, decision tree, k‐nearest neighbor, naïve Bayes, and multilayer perceptron) were used to further verify the predictive ability of the model. Forty‐one (12.9%) women experienced adverse maternal events, and 93 (29.2%) neonates experienced adverse neonatal events. Seven high‐risk factors were discovered in the maternal model, including New York Heart Association class, Eisenmenger syndrome, pulmonary hypertension, left ventricular ejection fraction, sinus tachycardia, arterial blood oxygen saturation, and pregnancy duration. The machine learning–based algorithms showed that the maternal model had an accuracy of 0.76 to 0.86 (area under the receiver operating characteristic curve=0.74–0.87) in the development cohort, and 0.72 to 0.86 (area under the receiver operating characteristic curve=0.68–0.80) in the validation cohort. Three high‐risk factors were discovered in the neonatal model, including Eisenmenger syndrome, preeclampsia, and arterial blood oxygen saturation. The machine learning–based algorithms showed that the neonatal model had an accuracy of 0.75 to 0.80 (area under the receiver operating characteristic curve=0.71–0.77) in the development cohort, and 0.72 to 0.79 (area under the receiver operating characteristic curve=0.69–0.76) in the validation cohort. Conclusions Two prenatal risk assessment models for both adverse maternal and neonatal events were established, which might assist clinicians in tailoring precise management and therapy in pregnant women with congenital heart disease.

Artificial periodic grooves on a metal line can mimic optical surface plasmon polaritons (SPPs) with high cut-off response at microwave frequencies, which is known as spoof SPPs and has recently gained great interest in biosensing due to the strong enhancement of the localized electric field. In this paper, a sensitive spoof SPP biosensor with split ring resonators (SRRs) for ovarian cancer diagnosis is reported for the first time. The SRRs are distributed in series on a metal line to replace the conventional periodic grooves, which maintains the same cut-off frequency and generates a sharp resonance simultaneously. This resonance enhances the localized field by a factor of 250 so that the proposed device is highly sensitive for detecting permittivity difference. The sensitivity in terms of frequency shifting was first investigated by electromagnetic simulation, and then demonstrated by distinguishing sucrose solutions with various concentrations. With the benefit of high sensitivity, the proposed biosensor successfully detects the serous ovarian cancer (SOC) and ovarian clear cell carcinoma (OCCC) tissues. The resonant frequency of the biosensor is 53.990 GHz intrinsically, which shifts to 53.814-53.968, 53.698-53.872, and 53.719-53.845 GHz with the normal, SOC, and OCCC tissues, respectively. The average point of all ovarian tissues is at 53.812 GHz, i.e., well below the normal tissues, which is a reasonable reference for cancer detection. Moreover, the average transmittance of all cancerous tissues is lower than the SOC tissues, which may be used to classify the SOC and OCCC tissues. The proposed planar SPP biosensor reveals a fast, sensitive, and label-free detection approach for ovarian cancers.

Photodynamic therapy (PDT) is considered an innovative and attractive modality to treat ovarian cancer. In the present study, a biodegradable polymer poly (ethylene glycol) (PEG)-poly (lactic acid)(PLA)-folate (FA-PEG-PLA) was prepared in order to synthesize an active-targeting, water-soluble and pharmacomodulated photosensitizer nanocarrier. Drug-loading content, encapsulation efficiency, in vitro and in vivo release were characterized, in which hypocrellin B (HB)/FA-PEG-PLA micelles had a high encapsulation efficiency and much slower control release for drugs compared to free drugs (P

Small extracellular vesicles (sEVs) are considered as potential markers for tumor detection and vehicles for tumor treatment. Here we develop a deep learning based nanoparticle analyzer that can measure label-free sEVs. Light sheet technology is adopted to illuminate single nanoparticles on chip that suppresses the background noise. A deep learning method for nanoscale particle tracking is demonstrated, which accurately obtains the particle size distribution of polystyrene beads as small as 41 nm in diameter. Small extracellular vesicles from normal and cancerous liver cell linage cells, and from sorafenib drug treated cancerous cells, are analyzed label-freely. It is shown that the three types of sEVs can be well differentiated by their particle size distributions. Our deep learning based small extracellular vesicles analyzer (DeepEVAnalyzer) not only provides a new technique for sEV-like nanoparticle analysis, but also demonstrates the potential of using sEVs as label-free marker for cancer diagnosis.

Human umbilical cord mesenchymal stem cells (huMSCs) can treat primary ovarian insufficiency (POI) related to ovarian granulosa cell (OGC) apoptosis caused by cisplatin chemotherapy. Exosomes are a class of membranous vesicles with diameters of 30–200 nm that are constitutively released by eukaryotic cells. Exosomes mediate local cell-to-cell communication by transferring microRNAs and proteins. In the present study, we demonstrated the effects of exosomes derived from huMSCs (huMSC-EXOs) on a cisplatin-induced OGC model in vitro and discussed the preliminary mechanisms involved in these effects. We successfully extracted huMSC-EXOs from huMSC culture supernatant and observed the effective uptake of exosomes by cells with fluorescent staining. Using flow cytometry (with annexin-V/PI labelling), we found that huMSC-EXOs increased the number of living cells. Western blotting showed that the expression of Bcl-2 and caspase-3 were upregulated, whilst the expression of Bax, cleaved caspase-3 and cleaved PARP were downregulated to protect OGCs. These results suggest that huMSC-EXOs can be used to prevent and treat chemotherapy-induced OGC apoptosis in vitro. Therefore, this work provides insight and further evidence of stem cell function and indicates that huMSC-EXOs protect OGCs from cisplatin-induced injury in vitro.

Optical imaging technology that has the advantages of high sensitivity and cost-effectiveness greatly promotes the progress of nondestructive single-cell studies. Complex cellular image analysis tasks such as three-dimensional reconstruction call for machine-learning technology in cell optical image research. With the rapid developments of high-throughput imaging flow cytometry, big data cell optical images are always obtained that may require machine learning for data analysis. In recent years, deep learning has been prevalent in the field of machine learning for large-scale image processing and analysis, which brings a new dawn for single-cell optical image studies with an explosive growth of data availability. Popular deep learning techniques offer new ideas for multimodal and multitask single-cell optical image research. This article provides an overview of the basic knowledge of deep learning and its applications in single-cell optical image studies. We explore the feasibility of applying deep learning techniques to single-cell optical image analysis, where popular techniques such as transfer learning, multimodal learning, multitask learning, and end-to-end learning have been reviewed. Image preprocessing and deep learning model training methods are then summarized. Applications based on deep learning techniques in the field of single-cell optical image studies are reviewed, which include image segmentation, super-resolution image reconstruction, cell tracking, cell counting, cross-modal image reconstruction, and design and control of cell imaging systems. In addition, deep learning in popular single-cell optical imaging techniques such as label-free cell optical imaging, high-content screening, and high-throughput optical imaging cytometry are also mentioned. Finally, the perspectives of deep learning technology for single-cell optical image analysis are discussed. © 2020 International Society for Advancement of Cytometry.

The detection of senescent cells becomes increasing important for tumor therapy and drug screening. Here a light sheet microfluidic cytometer with a disposable hydrodynamic focusing unit is developed for two dimensional (2D) light scattering measurements of single cells. The mixed polystyrene microspheres of 3.87 and 2.0 μm in diameter are successfully differentiated by our 2D light scattering microfluidic cytometer. The application of the 2D light scattering microfluidic cytometry for the label-free analysis of senescent cells without any labeling or staining is demonstrated by measurements of H2O2-treated U87 cells. Our light sheet-based 2D light scattering microfluidic cytometer is easy to assemble with a disposable hydrodynamic unit, which may find wild applications in clinics for label-free cell classification.

The subtyping of Acute lymphocytic leukemia (ALL) is important for proper treatment strategies and prognosis. Conventional methods for manual blood and bone marrow testing are time-consuming and labor-intensive, while recent flow cytometric immunophenotyping has the limitations such as high cost. Here we develop the deep learning-based light scattering imaging flow cytometry for label-free classification of ALL. The single ALL cells confined in three dimensional (3D) hydrodynamically focused stream are excited by light sheet. Our label-free microfluidic cytometry obtains big-data two dimensional (2D) light scattering patterns from single ALL cells of B/T subtypes. A deep learning framework named Inception V3-SIFT (Scale invariant feature transform)-Scattering Net (ISSC-Net) is developed, which can perform high-precision classification of T-ALL and B-ALL cell line cells with an accuracy of 0.993 ± 0.003. Our deep learning-based 2D light scattering flow cytometry is promising for automatic and accurate subtyping of un-stained ALL.

Background Ovarian cancer is the most leading cause of death and the third most common gynecologic malignancy in women. Traditional chemotherapy has inevitable drawbacks of nonspecific tumor targeting, high toxicity, and poor therapeutic efficiency. In order to overcome such shortcomings, we prepared a novel nano-carrier drug-delivery system to enhance the anti-tumor efficiency. Methods In vitro characterizations of nano-carriers were determined by TEM, DLS. Cell viability was measured by MTT method. RT-PCR was performed to measure the expression of FARα in three ovarian cancer cell lines. The drug-release study and the uptaken study were measured in vitro. The pharmacokinetic and the drug distribution study were verified by HPLC methods in vivo. The enhanced anti-tumor efficiency of FA-NP was evaluated by the tumor inhibitory rate in vivo. Results Paclitaxel (PTX)-loaded nanoparticles (NPs) (PTX-PEG-PLA-NP and PTX-PEG-PLA-FA-NP) were prepared successfully, and the drug-release study showed that the cumulative release rates of NP groups were much less than free PTX group. The pharmacokinetic study showed that the elimination phase of two kinds of NP groups were much longer than that of PTX group. The drug distribution in different tissues showed that the peak-reach time was 2 h in the PTX group and 6 h in both NP groups. All of these results confirmed the excellent slow-release effects of both kinds of nano-carriers. More importantly, we confirmed that PTX-PEG-PLA-FA-NP had greater uptake by SK-OV-3 cells than PTX-PEG-PLA-NP and free PTX in vitro. A drug-distribution study of tumor-bearing mice demonstrated that the PTX concentration of tumor tissues in the PTX-PEG-PLA-FA-NP group was 3 times higher than the other two groups. PTX-PEG-PLA-FA-NP was uptaken much more by SK-OV-3 cells than PTX-PEG-PLA-NP and free PTX. Eventually, based on the slow-release effect and tumor-targeting characteristics of PTX-PEG-PLA-FA-NP, a cytotoxicity test indicated that PTX-PEG-PLA-FA-NP was much more toxic to SK-OV-3 cells than the controls. The tumor inhibitory rate in the PTX-PEG-PLA-FA-NP group of tumor-bearing mice was about 1.5 times higher than the controls. The tumor targeting and anti-tumor efficiency of PTX-PEG-PLA-FA-NP were confirmed both in vitro and in vivo. Conclusions We developed an ovarian cancer targeting nano-carrier drug delivery system successfully, which showed perfect ovarian cancer targeting and anti-tumor effect, thus have the potential to be a new therapy strategy for ovarian cancer patients. Electronic supplementary material The online version of this article (10.1186/s13046-018-0700-z) contains supplementary material, which is available to authorized users.

Nanoparticles are promising novel drug delivery carriers that allow tumor targeting and controlled drug release. In the present study, we prepared poly butyl-cyanoacrylate nanoparticles (PBCA-NP) entrapped with hypocrellin B (HB) to improve the effect of photodynamic therapy (PDT) in ovarian cancer. An ovarian cancer ascites model using Fischer 344 rats and PBCA-NP entrapped with HB (HB-PBCA-NP) were formed successfully. The pharmacodynamic characteristics and biodistribution of the HB-PBCA-NP system were evaluated by comparison with HB dimethyl sulfoxide (HB-DMSO) and testing at various time-points following intraperitoneal drug administration. HB-PBCA-NP-based PDT combined with cytoreductive surgery was then administrated to the tumor-bearing animals. Kaplan-Meier survival analysis was performed to assess the therapeutic effect of the nanoparticle system. The serum HB concentration peaked 4 h after drug administration in the nanoparticle system, and 1 h with HB-DMSO. The peak exposure time of tumor tissues was also extended (4 vs. 2 h), and PBCA-NP remained present for much longer than HB-DMSO. Although PDT combined with surgery prolonged the survival time significantly compared with surgery alone (84 days, P

Conventional optical cytometric techniques usually measure fluorescence or scattering signals at fixed angles from flowing cells in a liquid stream. Here we develop a novel cytometer that employs a scanning optical fiber to illuminate single static cells on a glass slide, which requires neither microfluidic fabrication nor flow control. This static cytometric technique measures two dimensional (2D) light scattering patterns via a small numerical aperture (0.25) microscope objective for label-free single cell analysis. Good agreement is obtained between the yeast cell experimental and Mie theory simulated patterns. It is demonstrated that the static cytometer with a microscope objective of a low resolution around 1.30 μm has the potential to perform high resolution analysis on yeast cells with distributed sizes. The capability of the static cytometer for size determination with submicron resolution is validated via measurements on standard microspheres with mean diameters of 3.87 and 4.19 μm. Our 2D light scattering static cytometric technique may provide an easy-to-use, label-free, and flow-free method for single cell diagnostics. © 2015 International Society for Advancement of Cytometry

We develop an algorithm based on the finite-difference time-domain (FDTD) method to simulate light scattering from two-dimensional (2D) cell models. Our 2D FDTD code is implemented with the perfectly matched layer (PML) boundary condition. Simulation results from circular homogeneous models by our FDTD code agree well with published Mie theory results. The comparison between different 2D FDTD simulations shows that the light scattering is influenced by the size, number of organelles, and refractive index of the 2D cell models. Especially the changes of cell nucleus to cytoplasm volume ratio have strong influence to the large angle side and backward scattering. Our 2D FDTD algorithm provides a rapid simulation for the understanding of light scattering from cells.

Two-dimensional light scattering patterns of single normal granulocytes and acute leukemia cells (HL-60 cells) are obtained by using our label-free static cytometric technology. Conventional flow cytometry for the differentiation of these two types of cells is performed by measuring both the forward scattering (FSC) and side scattering (SSC). Our label-free static cytometer obtains the SSC patterns of single cells. By applying machine learning algorithm to the SSC patterns, a high accuracy rate for the classification of normal granulocytes and HL-60 cells is obtained. This may provide an automatic, label-free technique for leukemia analysis.

Two-dimensional (2D) light scattering cytometry has been demonstrated as an effective label-free technology for cell analysis. Here we develop the light-sheet illumination in 2D light scattering static cytometry. In our cytometer, a cylindrical lens is used to form the light-sheet for better excitation of the static cells under an inverted microscope. The thickness of the light-sheet measured in fluorescent solution is about 13 μm. Two-dimensional light scattering patterns of standard microspheres and yeast cells are obtained by using a complementary metal oxide semiconductor (CMOS) detector via a low numerical aperture (NA 0.4) optical objective. The experimental patterns characterized with fringe structures agree well with Mie theory simulated ones. Our results suggest that the light-sheet illumination is an effective excitation method for 2D light scattering label-free cytometry.

We have recently developed a 2D light scattering static cytometer for cellular analysis in a label-free manner, which measures side scatter (SSC) light in the polar angular range from 79 to 101 degrees. Compared with conventional flow cytometry, our cytometric technique requires no fluorescent labeling of the cells, and static cytometry measurements can be performed without flow control. In this paper we present an improved label-free static cytometer that can obtain 2D light scattering patterns in a wider angular range. By illuminating the static microspheres on chip with a scanning optical fiber, wide-angle 2D light scattering patterns of single standard microspheres with a mean diameter of 3.87 μm are obtained. The 2D patterns of 3.87 μm microspheres contain both large-angle forward scatter (FSC) and SSC light in the polar angular range from 40 to 100 degrees, approximately. Experimental 2D patterns of 3.87 μm microspheres are in good agreement with Mie theory simulated ones. The wide-angle light scattering measurements may provide a better resolution for particle analysis as compared with the SSC measurements. Two dimensional light scattering patterns of HL-60 human acute leukemia cells are obtained by using our static cytometer. Compared with SSC 2D light scattering patterns, wide-angle 2D patterns contain richer information of the HL-60 cells. The obtaining of 2D light scattering patterns in a wide angular range could help to enhance the capabilities of our label-free static cytometry for cell analysis.

Label-free microfluidic cytometry is of increasing interest for single cell analysis due to its advantages of high-throughput, miniaturization, as well as noninvasive detection. Here we develop a next generation label-free light-sheet microfluidic cytometer for single cell analysis by two-dimensional (2D) light scattering measurements. Our cytometer integrates light sheet illumination with a disposable hydrodynamic focusing unit, which can achieve 3D hydrodynamic focusing of a sample fluid to a diameter of 19 micrometer without microfabrication. This integration also improves the signal to noise ratio (SNR) for the acquisition of 2D light scattering patterns from label-free cells. Particle sizing with submicron resolution is achieved by our light-sheet flow cytometer, where Euclidean distance-based similarity measures are performed. Label-free, automatic classification of senescent and normal cells is achieved with a high accuracy rate by incorporating our light-sheet flow cytometry with support vector machine (SVM) algorithms. Our light-sheet microfluidic cytometry with a microfabrication-free hydrodynamic focusing unit may find wide applications for automatic and label-free clinical diagnosis.

We develop a pattern recognition cytometric technique for label-free cell classification. Two dimensional (2D) light scattering patterns from single cells and cell aggregates are obtained with a static cytometer. Good performance of the cytometric setup is verified by comparing yeast cell experimental results with theoretical simulations. Adaptive boosting (AdaBoost) method (a machine learning algorithm) is adopted for the analysis of the 2D light scattering patterns. It is shown that aggregates of three yeast cells can be well differentiated from aggregates of four yeast cells by this pattern recognition cytometric technique. We demonstrate that the pattern recognition cytometry can perform label-free classification of normal cervical cells and HeLa cells with a high accuracy rate.

In this paper, we mainly focus on automatic segmentation method of sequence kidney images from CT based on adaptive diffusion flow (ADF) method and morphological analysis. We modified the energy function by focusing on tangent direction diffusion which considered that the shapes of kidney mainly are convex. We design to evolve the dynamic curve with dual direction to improve the precision of segmentation and to deal with the local optimization problems. Experiments applied on kidney volumes show good property of segmentation using the proposed method.

Cytometry has wide applications in biomedicine for cell differentiation or disease monitoring. Here we report our newly developed two dimensional (2D) light scattering static cytometric technique for single and multiple cell analysis. The static cytometer adopts a scanning fiber probe for cell excitation and obtains 2D light scattering patterns on a complementary metal oxide semiconductor (CMOS) detector. Our results show that experimental 2D light scattering patterns obtained from single yeast cells are with fringe structure while those from multiple yeast cells give speckle patterns. The experimental results compare favorably with our 2D light scattering Mie theory simulations for both single and multiple cells. The varying of 2D light scattering patterns with different yeast cell clusters, either number or distribution changes, shows the potential of our 2D light scattering static cytometer for cellular diagnostics.

Background: We present an optical waveguide based cytometer that is capable of simultaneously collecting the light scattered by cells over a wide range of solid angles. Such comprehensive scattering data are a prerequisite for the microstructural characterization of cells. Methods: We use latex beads as cell mimics, and demonstrate the ability of this new cytometer to collect back-scattered light in two dimensions (2D). This cytometer is based on a liquid-core optical waveguide, excited by prism coupling, that also serves as the microfluidic channel. In principle, our use of a hemispherical lens allows the collection of scattered light from 0 to 180° in 2D. Results: The experimentally observed positions of the intensity peaks of the back-scattered light agree well with theoretical prediction of scattering from both 4.0- and 9.6-μm diameter latex beads. The position of the bead, relative to the axes of the hemispherical lens and the microchannel, strongly affects the scattering pattern. We discuss a computational method for determining these offsets. Conclusions: We show that wide-angle 2D light scattering patterns of cell-sized latex beads can be observed in a microfluidic-based optical cytometer that uses leaky waveguide mode excitation. This chip-based system is compatible with emerging chip-based technologies. © 2005 Wiley-Liss, Inc.