New trends in precision medicine: A pilot study of pure light scattering analysis as a useful tool for non-small cell lung cancer (nsclc) diagnosis

Background: To date, in personalized medicine approaches, single-cell analyses such as circulating tumour cells (CTC) are able to reveal small structural cell modifications, and therefore can retrieve several biophysical cell properties, such as the cell dimension, the dimensional relationship between the nucleus and the cytoplasm and the optical density of cellular sub-compartments. On this basis, we present in this study a new morphological measurement approach for the detection of vital CTC from pleural washing in individual non-small cell lung cancer (NSCLC) patients. Materials and methods: After a diagnosis of pulmonary malignancy, pleural washing was collected from nine NSCLC patients. The collected samples were processed with a density gradient separation process. Light scattering analysis was performed on a single cell. The results of this analysis were used to obtain the cell’s biophysical pattern and, later on, as basis for Machine Learning (ML) on unknown samples. Results: Morphological single-cell analysis followed by ML show a predictive picture for an NSCLC patient, screening that it is possible to distinguish CTC from other cells. Moreover, we find that the proposed measurement approach was fast, reliable, label-free, identifying and count CTC in a biological fluid. Conclusions: Our findings demonstrate that CTC Biophysical Profile by Pure Light Scattering in NSCLC could be used as a promising diagnostic candidate in NSCLC patients

Deep learning-enabled technologies for bioimage analysis.

Deep learning (DL) is a subfield of machine learning (ML), which has recently demonstrated its potency to significantly improve the quantification and classification workflows in biomedical and clinical applications. Among the end applications profoundly benefitting from DL, cellular morphology quantification is one of the pioneers. Here, we first briefly explain fundamental concepts in DL and then we review some of the emerging DL-enabled applications in cell morphology quantification in the fields of embryology, point-of-care ovulation testing, as a predictive tool for fetal heart pregnancy, cancer diagnostics via classification of cancer histology images, autosomal polycystic kidney disease, and chronic kidney diseases

A simple and robust event-detection algorithm for single-cell impedance cytometry

Microfluidic impedance cytometry is emerging as a powerful label-free technique for the characterization of single biological cells. In order to increase the sensitivity and the specificity of the technique, suited digital signal processing methods are required to extract meaningful information from measured impedance data. In this study, a simple and robust event-detection algorithm for impedance cytometry is presented. Since a differential measuring scheme is generally adopted, the signal recorded when a cell passes through the sensing region of the device exhibits a typical odd-symmetric pattern. This feature is exploited twice by the proposed algorithm: first, a preliminary segmentation, based on the correlation of the data stream with the simplest odd-symmetric template, is performed; then, the quality of detected events is established by evaluating their E2O index, that is, a measure of the ratio between their even and odd parts. A thorough performance analysis is reported, showing the robustness of the algorithm with respect to parameter choice and noise level. In terms of sensitivity and positive predictive value, an overall performance of 94.9% and 98.5%, respectively, was achieved on two datasets relevant to microfluidic chips with very different characteristics, considering three noise levels. The present algorithm can foster the role of impedance cytometry in single-cell analysis, which is the new frontier in "Omics.

Towards clinical translation of raman spectroscopy for tumor cell identification

In the modern world, cancer is one of the leading causes of death, and its early diagnostics remains one of the big challenges. Since cancer starts as a malfunction on the cellular level, the diagnostic techniques have to deal with single cells. Detection of circulating tumor cells (CTCs), which are present in the patient's blood, holds promise for the future theranostic applications, as CTCs represent the actual state of the primary tumor. Raman spectroscopy is a label-free technique capable of non-destructive and chemically-specific characterization of individual cells. In contrast to marker-based methods, the CTCs detected by Raman can be reused for more specific single-cells biochemical analysis methods. This thesis focuses on technological developments for Raman-based CTC identification, and encompasses the whole chain of involved methods and processes, including instrumentation and microfluidic cell handling, automation of spectra acquisition and storage, and chemometric data analysis. It starts with a design of custom application-specific instruments that we used to evaluate and optimize different experimental parameters. A major part is software development for automated acquisition and organized storage of spectral data in a database. With the automated measurement systems and the database in place, we were able to collect about 40.000 Raman spectra of more than 15 incubated cancer cell lines, healthy donor leukocytes, as well as samples originating from clinical patients. Additionally, the thesis gives an overview of data analysis methods and provides an insight into the underlying trends of the dataset. Although the cell identification models could not reliably differentiate between individual cancer cell lines, they were able to recognize tumor cells among healthy leukocytes with prediction accuracy of more than 95%. This work demonstrated an increase in the throughput of Raman-based CTC detection, and provides a basis for its clinical translation

Miniaturizing High Throughput Droplet Assays For Ultrasensitive Molecular Detection On A Portable Platform

Digital droplet assays – in which biological samples are compartmentalized into millions of femtoliter-volume droplets and interrogated individually – have generated enormous enthusiasm for their ability to detect biomarkers with single-molecule sensitivity. These assays have untapped potential for point-of-care diagnostics but are mainly confined to laboratory settings due to the instrumentation necessary to serially generate, control, and measure millions of compartments. To address this challenge, we developed an optofluidic platform that miniaturizes digital assays into a mobile format by parallelizing their operation. This technology has three key innovations: 1. the integration and parallel operation of hundred droplet generators onto a single chip that operates \u3e100x faster than a single droplet generator. 2. the fluorescence detection of droplets at \u3e100x faster than conventional in-flow detection using time-domain encoded mobile-phone imaging, and 3. the integration of on-chip delay lines and sample processing to allow serum-to-answer device operation. By using this time-domain modulation with cloud computing, we overcome the low framerate of digital imaging, and achieve throughputs of one million droplets per second. To demonstrate the power of this approach, we performed a duplex digital enzyme-linked immunosorbent assay (ELISA) in serum to show a 1000x improvement over standard ELISA and matching that of the existing laboratory-based gold standard digital ELISA system. This work has broad potential for ultrasensitive, highly multiplexed detection, in a mobile format. Building on our initial demonstration, we explored the following: (i) we demonstrated that the platform can be extended to \u3e100x multiplexing by using time-domain encoded light sources to detect color-coded beads that each correspond to a unique assay, (ii) we demonstrated that the platform can be extended to the detection of nucleic acid by implementing polymerase chain reaction, and (iii) we demonstrated that sensitivity can be improved with a nanoparticle-enhanced ELISA. Clinical applications can be expanded to measure numerous biomarkers simultaneously such as surface markers, proteins, and nucleic acids. Ultimately, by building a robust device, suitable for low-cost implementation with ultrasensitive capabilities, this platform can be used as a tool to quantify numerous medical conditions and help physicians choose optimal treatment strategies to enable personalized medicine in a cost-effective manner

Understanding Mechanisms of Metastasis of Aggressive Breast Cancers via Microfluidic Means

The spread of cancer from its site of origin to other organs is called metastasis, and it is this stage of the disease that is responsible for over 90% of cancer deaths. Tumors are comprised of a heterogeneous population and not every cell in a primary tumor has the intrinsic capability to metastasize. Understanding what gives certain metastatically enabled cells this potential will ultimately provide insight into how to target and prevent metastases. In order to form a metastasis, a cancer cell must: move, invade through often stiff supporting tissue, enter the vasculature via small intercellular spaces, survive the hydrodynamic forces of circulation, squeeze through vessel endothelium once again, and finally proliferate. Imbued with the knowledge of this metastatic journey of a cancer cell, it is understandable how very physical and mechanical in nature the process is. Therefore, to study the steps of metastasis effectively requires the ability to precisely control physical attributes of a cell’s surroundings. The engineering field of microfluidics affords this opportunity and in this work I advanced our present knowledge of the metastatic process by using microfluidic techniques in four fundament studies of critical steps required for metastases. In one study, cancer cells are challenged with a geometrically confining migration space which mimics the constraints of a lymphatic capillary and the early necessary intravasation metastatic step. After migration, motile and non-motile cells are recaptured and analyzed for genetic differences which allow for intravasation. In another study, the effects of secreted factors from normal immune cells in the tumor microenvironment are tested for their stimulation of cancer cell migration – the first required step of metastasis – in the most aggressive form of breast cancer that is considered metastatic at its inception. A third study leveraged the adhesive properties of cancer cells as a novel paradigm for circulating tumor cell capture and analysis independent of dynamic cell surface markers. Lastly, specifically designed microfluidic assays were used to determine a multiparametric cellular phenotype of the most aggressive subpopulation of cancer cells’ biomechanical properties, which may confer the capability to effectively traverse the inefficient steps of metastasis.PHDCellular & Molec Biology PhDUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/143962/1/allensg_1.pd

Microdevices and Microsystems for Cell Manipulation

Microfabricated devices and systems capable of micromanipulation are well-suited for the manipulation of cells. These technologies are capable of a variety of functions, including cell trapping, cell sorting, cell culturing, and cell surgery, often at single-cell or sub-cellular resolution. These functionalities are achieved through a variety of mechanisms, including mechanical, electrical, magnetic, optical, and thermal forces. The operations that these microdevices and microsystems enable are relevant to many areas of biomedical research, including tissue engineering, cellular therapeutics, drug discovery, and diagnostics. This Special Issue will highlight recent advances in the field of cellular manipulation. Technologies capable of parallel single-cell manipulation are of special interest

COMPUTATIONAL ANALYSIS OF CODE-MULTIPLEXED COULTER SENSOR SIGNALS

Nowadays, lab-on-a-chip (LoC) technology has been applied in a variety of applications because of its capability to perform accurate microscale manipulations of cells for point-of-care diagnostics. On the other hand, such a result is not readily available from an LoC device and typically still requires a post-inspection of the chip using traditional laboratory equipment such as a microscope, negating the advantages of the LoC technology. To solve this dilemma, my doctoral research mainly focuses on developing portable and disposable biosensors for interfacing with and digitizing the information from an LoC system. Our sensor platform, integrated with multiple microfluidic impedance sensors, electrically monitors and tracks manipulated cells on an LoC device. The sensor platform compresses information from each sensor into a 1-dimensional electrical waveform, and therefore, further signal processing is required to recover the readout of each sensor and extract information of detected cells. Furthermore, with the capability of the sensor platform, we have introduced integrated microfluidic cytometers to characterize properties of cells such as cell surface expression and mechanical properties.Ph.D

Biosensors for Diagnosis and Monitoring

Biosensor technologies have received a great amount of interest in recent decades, and this has especially been the case in recent years due to the health alert caused by the COVID-19 pandemic. The sensor platform market has grown in recent decades, and the COVID-19 outbreak has led to an increase in the demand for home diagnostics and point-of-care systems. With the evolution of biosensor technology towards portable platforms with a lower cost on-site analysis and a rapid selective and sensitive response, a larger market has opened up for this technology. The evolution of biosensor systems has the opportunity to change classic analysis towards real-time and in situ detection systems, with platforms such as point-of-care and wearables as well as implantable sensors to decentralize chemical and biological analysis, thus reducing industrial and medical costs. This book is dedicated to all the research related to biosensor technologies. Reviews, perspective articles, and research articles in different biosensing areas such as wearable sensors, point-of-care platforms, and pathogen detection for biomedical applications as well as environmental monitoring will introduce the reader to these relevant topics. This book is aimed at scientists and professionals working in the field of biosensors and also provides essential knowledge for students who want to enter the field

The Translational Status of Cancer Liquid Biopsies

Precision oncology aims to tailor clinical decisions specifically to patients with the objective of improving treatment outcomes. This can be achieved by leveraging omics information for accurate molecular characterization of tumors. Tumor tissue biopsies are currently the main source of information for molecular profiling. However, biopsies are invasive and limited in resolving spatiotemporal heterogeneity in tumor tissues. Alternative non-invasive liquid biopsies can exploit patient’s body fluids to access multiple layers of tumor-specific biological information (genomes, epigenomes, transcriptomes, proteomes, metabolomes, circulating tumor cells, and exosomes). Analysis and integration of these large and diverse datasets using statistical and machine learning approaches can yield important insights into tumor biology and lead to discovery of new diagnostic, predictive, and prognostic biomarkers. Translation of these new diagnostic tools into standard clinical practice could transform oncology, as demonstrated by a number of liquid biopsy assays already entering clinical use. In this review, we highlight successes and challenges facing the rapidly evolving field of cancer biomarker research. Lay Summary: Precision oncology aims to tailor clinical decisions specifically to patients with the objective of improving treatment outcomes. The discovery of biomarkers for precision oncology has been accelerated by high-throughput experimental and computational methods, which can inform fine-grained characterization of tumors for clinical decision-making. Moreover, advances in the liquid biopsy field allow non-invasive sampling of patient’s body fluids with the aim of analyzing circulating biomarkers, obviating the need for invasive tumor tissue biopsies. In this review, we highlight successes and challenges facing the rapidly evolving field of liquid biopsy cancer biomarker research