Cell sorting in cancer research--diminishing degree of cell heterogeneity

Deformability of Heterogeneous Red Blood Cells in Aging and Related Pathologies

Aging is interrelated with changes in red blood cell parameters and functionality. In this article, we focus on red blood cells (RBCs) and provide a review of the known changes associated with the characterization of RBC deformability in aging and related pathologies. The biophysical parameters complement the commonly used biochemical parameters and may contribute to a better understanding of the aging process. The power of the deformability measurement approach is well established in clinical settings. Measuring RBCs' deformability has the advantage of relative simplicity, and it reflects the complex effects developing in erythrocytes during aging. However, aging and related pathological conditions also promote heterogeneity of RBC features and have a certain impact on the variance in erythrocyte cell properties. The possible applications of deformability as an early biophysical biomarker of pathological states are discussed, and modulating PIEZO1 as a therapeutic target is suggested. The changes in RBCs' shape can serve as a proxy for deformability evaluation, leveraging single-cell analysis with imaging flow cytometry and artificial intelligence algorithms. The characterization of biophysical parameters of RBCs is in progress in humans and will provide a better understanding of the complex dynamics of aging.

Image-based cell sorting using focused travelling surface acoustic waves

Sorting cells is an essential primary step in many biological and clinical applications such as high-throughput drug screening, cancer research and cell transplantation. Cell sorting based on their mechanical properties has long been considered as a promising label-free biomarker that could revolutionize the isolation of cells from heterogeneous populations. Recent advances in microfluidic image-based cell analysis combined with subsequent label-free sorting by on-chip actuators demonstrated the possibility of sorting cells based on their physical properties. However, the high purity of sorting is achieved at the expense of a sorting rate that lags behind the analysis throughput. Furthermore, stable and reliable system operation is an important feature in enabling the sorting of small cell fractions from a concentrated heterogeneous population. Here, we present a label-free cell sorting method, based on the use of focused travelling surface acoustic wave (FTSAW) in combination with real-time deformability cytometry (RT-DC). We demonstrate the flexibility and applicability of the method by sorting distinct blood cell types, cell lines and particles based on different physical parameters. Finally, we present a new strategy to sort cells based on their mechanical properties. Our system enables the sorting of up to 400 particles per s. Sorting is therefore possible at high cell concentrations (up to 36 million per ml) while retaining high purity (>92%) for cells with diverse sizes and mechanical properties moving in a highly viscous buffer. Sorting of small cell fraction from a heterogeneous population prepared by processing of small sample volume (10 μl) is also possible and here demonstrated by the 667-fold enrichment of white blood cells (WBCs) from raw diluted whole blood in a continuous 10-hour sorting experiment. The real-time analysis of multiple parameters together with the high sensitivity and high-throughput of our method thus enables new biological and therapeutic applications in the future.

Automated cell-type classification combining dilated convolutional neural networks with label-free acoustic sensing

This study aimed to automatically classify live cells based on their cell type by analyzing the patterns of backscattered signals of cells with minimal effect on normal cell physiology and activity. Our previous studies have demonstrated that label-free acoustic sensing using high-frequency ultrasound at a high pulse repetition frequency (PRF) can capture and analyze a single object from a heterogeneous sample. However, eliminating possible errors in the manual setting and time-consuming processes when postprocessing integrated backscattering (IB) coefficients of backscattered signals is crucial. In this study, an automated cell-type classification system that combines a label-free acoustic sensing technique with deep learning-empowered artificial intelligence models is proposed. We applied an one-dimensional (1D) convolutional autoencoder to denoise the signals and conducted data augmentation based on Gaussian noise injection to enhance the robustness of the proposed classification system to noise. Subsequently, denoised backscattered signals were classified into specific cell types using convolutional neural network (CNN) models for three types of signal data representations, including 1D CNN models for waveform and frequency spectrum analysis and two-dimensional (2D) CNN models for spectrogram analysis. We evaluated the proposed system by classifying two types of cells (e.g., RBC and PNT1A) and two types of polystyrene microspheres by analyzing their backscattered signal patterns. We attempted to discover cell physical properties reflected on backscattered signals by controlling experimental variables, such as diameter and structure material. We further evaluated the effectiveness of the neural network models and efficacy of data representations by comparing their accuracy with that of baseline methods. Therefore, the proposed system can be used to classify reliably and precisely several cell types with different intrinsic physical properties for personalized cancer medicine development.

To Die or Not to Die—Regulated Cell Death and Survival in Cyanobacteria

Regulated cell death (RCD) is central to the development, integrity, and functionality of multicellular organisms. In the last decade, evidence has accumulated that RCD is a universal phenomenon in all life domains. Cyanobacteria are of specific interest due to their importance in aquatic and terrestrial habitats and their role as primary producers in global nutrient cycling. Current knowledge on cyanobacterial RCD is based mainly on biochemical and morphological observations, often by methods directly transferred from vertebrate research and with limited understanding of the molecular genetic basis. However, the metabolism of different cyanobacteria groups relies on photosynthesis and nitrogen fixation, whereas mitochondria are the central executioner of cell death in vertebrates. Moreover, cyanobacteria chosen as biological models in RCD studies are mainly colonial or filamentous multicellular organisms. On the other hand, unicellular cyanobacteria have regulated programs of cellular survival (RCS) such as chlorosis and post-chlorosis resuscitation. The co-existence of different genetically regulated programs in cyanobacterial populations may have been a top engine in life diversification. Development of cyanobacteria-specific methods for identification and characterization of RCD and wider use of single-cell analysis combined with intelligent image-based cell sorting and metagenomics would shed more light on the underlying molecular mechanisms and help us to address the complex colonial interactions during these events. In this review, we focus on the functional implications of RCD in cyanobacterial communities.

Autofluorescence of NADH is a new biomarker for sorting and characterizing cancer stem cells in human glioma

Background

The existing cell surface markers used for sorting glioma stem cells (GSCs) have obvious limitations, such as vulnerability to the enzymatic digestion and time-consuming labeling procedure. Reduced nicotinamide adenine dinucleotide (NADH) as a cellular metabolite with property of autofluorescence has the potential to be used as a new biomarker for sorting GSCs.

Methods

A method for sorting GSCs was established according to the properties of the autofluorescence of NADH. Then, the NADH^high and NADH^low subpopulations were sorted. The stem-like properties of the subpopulations were evaluated by qRT-PCR, western blot analyses, limiting dilution assay, cell viability assay, bioluminescence imaging, and immunofluorescence analysis in vitro and in vivo. The relationship between CD133⁺/CD15⁺ cells and NADH^high subpopulation was also assessed.

Results

NADH^high cells expressed higher stem-related genes, formed more tumor spheres, and harbored stronger pluripotency in vitro and higher tumorigenicity in vivo, compared to NADH^low subpopulation. NADH^high glioma cells had the similar stemness with CD133⁺ or CD15⁺ GSCs, but the three subpopulations less overlaid each other. Also, NADH^high glioma cells were more invasive and more resistant to chemotherapeutic drug temozolomide (TMZ) than NADH^low cells. In addition, the autofluorescence of NADH might be an appropriate marker to sort cancer stem cells (CSCs) in other cancer types, such as breast and colon cancer.

Conclusion

Our findings demonstrate that intracellular autofluorescence of NADH is a non-labeling, sensitive maker for isolating GSCs, even for other CSCs.

Heterogeneity of Metazoan Cells and Beyond: To Integrative Analysis of Cellular Populations at Single-Cell Level

In this paper, we review some of the recent advances in cellular heterogeneity and single-cell analysis methods. In modern research of cellular heterogeneity, there are four major approaches: analysis of pooled samples, single-cell analysis, high-throughput single-cell analysis, and lately integrated analysis of cellular population at a single-cell level. Recently developed high-throughput single-cell genetic analysis methods such as RNA-Seq require purification step and destruction of an analyzed cell often are providing a snapshot of the investigated cell without spatiotemporal context. Correlative analysis of multiparameter morphological, functional, and molecular information is important for differentiation of more uniform groups in the spectrum of different cell types. Simplified distributions (histograms and 2D plots) can underrepresent biologically significant subpopulations. Future directions may include the development of nondestructive methods for dissecting molecular events in intact cells, simultaneous correlative cellular analysis of phenotypic and molecular features by hybrid technologies such as imaging flow cytometry, and further progress in supervised and non-supervised statistical analysis algorithms.

Systems medicine in colorectal cancer: from a mathematical model toward a new type of clinical trial

Current colorectal cancer (CRC) treatment guidelines are primarily based on clinical features, such as cancer stage and grade. However, outcomes may be improved using molecular treatment guidelines. Potentially useful biomarkers include driver mutations and somatically inherited alterations, signaling proteins (their expression levels and (post) translational modifications), mRNAs, micro-RNAs and long noncoding RNAs. Moving to an integrated system is potentially very relevant. To implement such an integrated system: we focus on an important region of the signaling network, immediately above the G1-S restriction point, and discuss the reconstruction of a Molecular Interaction Map and interrogating it with a dynamic mathematical model. Extensive model pretraining achieved satisfactory, validated, performance. The model helps to propose future target combination priorities, and restricts drastically the number of drugs to be finally tested at a cellular, in vivo, and clinical-trial level. Our model allows for the inclusion of the unique molecular profiles of each individual patient's tumor. While existing clinical guidelines are well established, dynamic modeling may be used for future targeted combination therapies, which may progressively become part of clinical practice within the near future. WIREs Syst Biol Med 2016, 8:314-336. doi: 10.1002/wsbm.1342 For further resources related to this article, please visit the WIREs website.

Highly focused high-frequency travelling surface acoustic waves (SAW) for rapid single-particle sorting

High-speed sorting is an essential process in a number of clinical and research applications, where single cells, droplets and particles are segregated based on their properties in a continuous flow. With recent developments in the field of microscale actuation, there is increasing interest in replicating the functions available to conventional fluorescence activated cell sorting (FACS) flow cytometry in integrated on-chip systems, which have substantial advantages in cost and portability. Surface acoustic wave (SAW) devices are ideal for many acoustofluidic applications, and have been used to perform such sorting at rates on the order of kHz. Essential to the accuracy of this sorting, however, is the dimensions of the region over which sorting occurs, where a smaller sorting region can largely avoid inaccurate sorting across a range of sample concentrations. Here we demonstrate the use of flow focusing and a highly focused SAW generated by a high-frequency (386 MHz), 10 μm wavelength set of focused interdigital transducers (FIDTs) on a piezoelectric lithium niobate substrate, yielding an effective sorting region only ~25 μm wide, with sub-millisecond pulses generated at up to kHz rates. Furthermore, because of the use of high frequencies, actuation of particles as small as 2 μm can be realized. Such devices represent a substantial step forward in the evolution of highly localized forces for lab-on-a-chip microfluidic applications.

Co-fabrication of chitosan and epoxy photoresist to form microwell arrays with permeable hydrogel bottoms

Microfabrication technology offers the potential to create biological platforms with customizable patterns and surface chemistries, allowing precise control over the biochemical microenvironment to which a cell or group of cells is exposed. However, most microfabricated platforms grow cells on impermeable surfaces. This report describes the co-fabrication of a micropatterned epoxy photoresist film with a chitosan film to create a freestanding array of permeable, hydrogel-bottomed microwells. These films possess optical properties ideal for microscopy applications, and the chitosan layers are semi-permeable with a molecular exclusion of 9.9 ± 2.1 kDa. By seeding cells into the microwells, overlaying inert mineral oil, and supplying media via the bottom surface, this hybrid film permits cells to be physically isolated from one another but maintained in culture for at least 4 days. Arrays co-fabricated using these materials reduce both large-molecular-weight biochemical crosstalk between cells and mixing of different clonal populations, and will enable high-throughput studies of cellular heterogeneity with increased ability to customize dynamic interrogations compared to materials in currently available technologies.

Recapitulation and Modulation of the Cellular Architecture of a User-Chosen Cell of Interest Using Cell-Derived, Biomimetic Patterning

Heterogeneity of cell populations can confound population-averaged measurements and obscure important findings or foster inaccurate conclusions. The ability to generate a homogeneous cell population, at least with respect to a chosen trait, could significantly aid basic biological research and development of high-throughput assays. Accordingly, we developed a high-resolution, image-based patterning strategy to produce arrays of single-cell patterns derived from the morphology or adhesion site arrangement of user-chosen cells of interest (COIs). Cells cultured on both cell-derived patterns displayed a cellular architecture defined by their morphology, adhesive state, cytoskeletal organization, and nuclear properties that quantitatively recapitulated the COIs that defined the patterns. Furthermore, slight modifications to pattern design allowed for suppression of specific actin stress fibers and direct modulation of adhesion site dynamics. This approach to patterning provides a strategy to produce a more homogeneous cell population, decouple the influences of cytoskeletal structure, adhesion dynamics, and intracellular tension on mechanotransduction-mediated processes, and a platform for high-throughput cellular assays.

The Single-Cell Lab or How to Perform Single-Cell Molecular Analysis

The increasing interest towards cellular heterogeneity within cell populations has pushed the development of new protocols to isolate and analyze single cells. PCR-based amplification techniques are widely used in this field. However, setting up an experiment and analyzing the results can sometimes be challenging. The aim of this chapter is to provide a general overview on single-cell PCR analysis focusing on the potential pitfalls and on the possible solutions to successfully perform the analysis.

The microfluidic multitrap nanophysiometer for hematologic cancer cell characterization reveals temporal sensitivity of the calcein-AM efflux assay

Cytometric studies utilizing flow cytometry or multi-well culture plate fluorometry are often limited by a deficit in temporal resolution and a lack of single cell consideration. Unfortunately, many cellular processes, including signaling, motility, and molecular transport, occur transiently over relatively short periods of time and at different magnitudes between cells. Here we demonstrate the multitrap nanophysiometer (MTNP), a low-volume microfluidic platform housing an array of cell traps, as an effective tool that can be used to study individual unattached cells over time with precise control over the intercellular microenvironment. We show how the MTNP platform can be used for hematologic cancer cell characterization by measuring single T cell levels of CRAC channel modulation, non-translational motility, and ABC-transporter inhibition via a calcein-AM efflux assay. The transporter data indicate that Jurkat T cells exposed to indomethacin continue to accumulate fluorescent calcein for over 60 minutes after calcein-AM is removed from the extracellular space.

Multiscale design of cell-type-specific pharmacokinetic/pharmacodynamic models for personalized medicine: application to temozolomide in brain tumors

Optimizing anticancer therapeutics needs to account for variable drug responses in heterogeneous cell populations within the tumor as well as in organs of toxicity. To address cell heterogeneity, we propose a multiscale modeling approach—from in vitro to preclinical and clinical studies—to develop cell-type–specific pharmacokinetic–pharmacodynamic (PK-PD) models. A physiologically based mechanistic modeling approach integrating data from aqueous solutions, U87 glioma cells, mice, and cancer patients was utilized to characterize the brain disposition of temozolomide (TMZ), the cornerstone of chemotherapy against glioblastoma multiforme. The final model represented intracellular normal brain and brain tumor compartments in which TMZ pH-dependent conversion to the DNA-alkylating species leads to the formation of DNA adducts that serve as an entry point for a PD model. This multiscale protocol can be extended to account for TMZ PK-PD in different cell populations, thus providing a critical tool to personalize TMZ-based chemotherapy on a cell-type–specific basis.