Going with the flow

The current advancements in medical research, as well as those in the diagnostic capabilities provided by flow cytometric technology, are provoking a shift in the flow cytometric business landscape.

Scan it, see it, measure it, analyze it. That could be a mantra for imaging flow cytometry (IFC). Over the last decade, IFC has opened a new window on biological and medical research by offering capabilities that are not possible with traditional flow cytometry. There have also been many developments in reagents, instruments, and applications. IFC combines the high-throughput, multiparameter capabilities of conventional flow cytometry with morphological and spatial information, all at single-cell resolution. Multichannel digital images of hundreds of thousands of individual cells can be captured within minutes, and include several fluorescence channels as well as bright field (transmitted light) and dark field (scattered light). The throughput of IFC means that it is especially well suited to the analysis of rare cell types like circulating tumor cells (which are cancer cells that escaped from a primary tumor and circulate in the bloodstream) and transition states, such as cell cycle phases (mitosis).

Although this technology comes with decades of hardware experience, ongoing improvements in the algorithms really expand how scientists can apply IFC.

In some cases, getting more from IFC means higher cell numbers or more feature information from a sample. For instance, tens to hundreds of thousands of cells can be analyzed, and some platforms can track up to 20 different fluorescent colors.

Getting the statistics right, tracking more signals, and so on, all move forward with enhancements to the IFC hardware and software.

Software advances
With lasers exciting markers, optics gathering images, detectors collecting the fluorescence, and a system keeping cells moving in the device, the hardware requirements for IFC cannot be denied. For example, improvements in charge-coupled device (CCD) cameras impact the performance of an IFC platform. This technology platform benefits from improvements in CCD-camera sensitivity, CCD-camera image-capture times, computer processing speeds, and data storage. IFC also benefits from enhanced microscope- and flow-cytometry applications, new antibody development, and new fluorochrome development. But scientists cannot get much out of the raw data without more work. Fortunately, the software can facilitate analysis by making complex image-processing tasks simpler, faster, and easier to understand. Plus, as software improves, scientists can do more with IFC.

IFC benefits from improvements made to computer algorithms that can identify unique morphologies through artificial intelligence (IA), deep learning, or segmentation and feature calculation. Most of the recent enhancements to IFC have been centered on the data-analysis side by creating new machine-learning tools. This has allowed scientists to leverage the rapid development in AI, and apply it to cell-based assays and biological research.

To analyze information more completely, open-source software tools offer new opportunities. IFC is seeing a paradigm shift from low- to high-information-content analysis, driven partly by deep learning algorithms. Scientists predict a wealth of applications with potential translation into clinical practice.

Slowing down time
With cells moving at 1–10 meters per second, unless the camera has a very fast shutter speed, the image will be blurred. To accurately image cells at high-speed flow, recently scientists developed time-stretch IFC and combined it with deep learning. The time-stretch microscope captures blur-free images with a shutter speed of a billionth of a second then slows the images in time so they can be detected and digitized.

A few months ago, researchers reported their latest results, in which the computation time was reduced from seconds to less than a millisecond. This allows the AI algorithm to classify the cells in flow and direct a cell sorter to separate the cancer cells from normal cells before the cells exit the instrument. Genetic studies, such as DNA sequencing can then be done on separated cells to identify the genetic makeup of the cancer and design a personalized treatment for the patient.

That combination of technologies drives penetrating analysis. The hardware starts the IFC process and the software determines how much can be learned from the data. With ongoing improvements in the analysis, IFC will surely deliver advances beyond today’s vision.

Opportunities ahead
Tremendous progress has been made in each paradigm of cell enumeration. Several industries and clinical and research applications leverage the use of impedance, optical, or image-analysis systems for cell counting and characterization. Each method provides advantages that need to be considered, while keeping in mind the cost, throughput, accuracy, and portability of the device.

Many potential clinical uses of IFC are conceivable – for instance, a differential diagnosis of acute and chronic lymphocytic/myeloid leukemia, possibly using fewer biomarkers, or ideally unstained and unmanipulated blood samples. Reducing the number of required descriptive biomarkers would greatly simplify the laboratory sample preparation, and help preserve the intact nativeness of samples, which are often fragile in hematological diseases. Recently, IFC was shown to deliver integrated leukemia diagnostics in one test.

Another potential clinical use of IFC is to analyze bodily fluids for rare cells, for example, the typically small number of leukemic cells that remain in the patient during treatment, known as minimal residual disease. There is substantial interest in liquid biopsy, the analysis of circulating tumor cells, which are extremely rare. Liquid biopsy might detect cancer at an early stage, circulating metastatic or drug-resistant neoplastic cells, clotting abnormalities of platelet microparticles, or fetal abnormalities. A major advantage of the liquid biopsy is that it can be carried out in a simple, noninvasive way. One reason it took the liquid biopsy so long to develop is that circulating tumor cells are found in low concentrations in the bloodstream. IFC in combination with machine learning has the potential to identify a single tumor cell out of millions of cells with unprecedented accuracy. Blood specimens can then be sampled in serial fashion, providing a more comprehensive monitoring of a patient’s cancer than can be obtained through traditional methods, as was demonstrated for hepatocellular carcinoma. Liquid biopsies could also be analyzed by imaging technologies other than IFC. Recently, multiplex protein detection on circulating tumor cells using imaging mass cytometry has been demonstrated.

Despite these promises, IFC is currently primarily used in research rather than clinical practice. Experts see data analysis as the primary hurdle – it is often prone to variation, manual tuning, and interpretation. These issues might be overcome with machine learning approaches. As well, there is a need for standardization of IFC, which should include standard operating procedures and standardized quality control of hardware performance. Although a common practice for conventional flow cytometry, this has not yet been implemented as such in IFC.

User-friendly, robust, and standardized workflows that can facilitate machine learning, especially deep learning, will accelerate the paradigm shift from low- to high-content analysis in IFC. Furthermore, cloud computing can overcome the computational infrastructure hurdles. These developments are key for practical IFC applications to reach the clinic, fueling the applicability of IFC as a diagnostic, prognostic, and therapeutic tool.

Affordability developments
Further developments, enabling affordable flow cytometry, include new laser diode-packaging technologies that improve manufacturing and package thermal resistance, as well as submillimeter proximity of various wavelength laser dies. The RGB lasers, used as pico projectors for head-up display and near-to-eye virtual and augmented reality, require a tiny form factor.

These same technologies could be applied to medical devices, and could enable pocket-sized flow cytometers that could be delivered to a sick patient in a remote area, or used for continuous monitoring of patients in ambulances. These potentially life-saving devices would require a multiwavelength laser module that is integrated into a compact package.

To detect the forward signal, a large-area p-i-n photodiode is commonly chosen. This photodiode is a relatively simple component, delivering a photocurrent based on the photoelectric effect on a p-n junction under a reverse voltage. New advancements driven by the need for miniaturization of head-mounted virtual and augmented reality devices include integrated dark-current suppression, thermal compensation, and signal amplifiers, as well as built-in digital signal-output conversion and voltage output within the same small (mm) packing size. A precursor of these new developments is the tiny ambient SFH 5711 light sensor. All of these features increase the signal-to-noise ratio and the accuracy of the reading.

Most flow cytometers use photomultiplier tubes for detecting the scattered light signal, because a much higher signal-to-noise ratio is required to detect very faint light scattered by single cells. In this part of the flow cytometer, substitutes for the bulky photomultiplier tubes include avalanche photodiodes (APDs), single-photon avalanche diodes (SPADs), and silicon photomultipliers (SiPMs). These semiconductor-based sensors are superior in performance and much more compact and robust than the photomultiplier tubes. Although APDs, SPADs, and SiPMs are lower in cost than the photomultiplier tubes, they are still relatively expensive because they are only found in low-volume, niche applications.

With advancements in lidar technology for autonomous vehicles and robotics, which need powerful 3D range and depth sensing, the market for very sensitive detectors with low intrinsic noise and high photocurrents is growing. APDs and SiPMs are the most likely choices for 905-nm pulsed lasers to extend the sensing range of the lidar system because of their high intrinsic gain. The growing market for autonomous vehicles and the large number of sensors required for one vehicle will ultimately not only drive technology improvements, but also reduce cost. So autonomous driving technology could indirectly help cure cancer by driving down the price of flow cytometers through the replacement of photomultiplier tubes with newer sensors.

These advancements are helping to make flow cytometry tools smaller, more powerful, and more affordable, leading to countless new research opportunities for scientists. The future of cancer therapy could include customized analysis, using flow cytometry to diagnose cancer stages and treat specific cancer types. The heterogeneity of cancer cells and their antibody responses are substantial and the immune response is complex. In the future, flow cytometry use could occur at the point of care with a tabletop or portable tool. Small, semi-complex flow cytometry devices may also be possible, with a cloud data link and AI-driven suggestions for sample preparation and appropriate antigens and fluorochromes for optimized results.

Meaningfully interpreting the readings from a large amount of available laser excitation wavelengths can be challenging. Software solutions have become a large portion of the size of the flow cytometry market, which was estimated to reach USD 8 billion by 2025. Despite these large market numbers, most of the actual tools are being used for research and development and not for point-of-care of patients.

Way forward
The current advancements in medical research, as well as those in the diagnostic capabilities provided by flow cytometric technology, will provoke a shift in the flow cytometric business landscape. Instead of the technology being used only for research (with cumbersome, expensive tools), the market can be revolutionized by less complex tools that rely on cloud computing and AI to assist medical personnel in making diagnostic and therapeutic decisions for patients.