Sphere Fluidics, a company developing single cell analysis systems underpinned by its patented picodroplet technology, and Heriot-Watt University, a specialist, pioneering Scottish University, have been awarded a Knowledge Transfer Partnership (KTP) grant from Innovate UK, the UK’s innovation agency. The grant will facilitate the development of novel droplet generator instrumentation, which will be used to expand Sphere Fluidics’ portfolio of microfluidic instruments for advanced biologics discovery and therapeutic cell line development.
Awarded to promote the collaboration of knowledge, technology and skills within the UK Knowledge Base, the KTP has been granted to Sphere Fluidics, in partnership with Dr. Graeme Whyte, Associate Professor at Heriot-Watt University. The two-year project will develop next-generation intelligent instrumentation and advance research across a range of picodroplet techniques, allowing scientists to discover rare cell phenotypes and to help to solve a range of biological questions ranging from antibody discovery to antimicrobial resistance, enzyme evolution and synthetic biology. The novel platform for semi-automated picodroplet production will be employed by the company to improve control of droplet production, using advanced imaging technology.
As part of the project, Dr. John McGrath has been appointed to Sphere Fluidics’ team as a Research Scientist in physics and engineering, to support the transfer of cutting-edge research into the company’s portfolio of single-cell analysis instruments, including for several new commercial products.
Dr. Marian Rehak, VP of Research and Development at Sphere Fluidics, said: “This innovative project with Heriot-Watt University, will bring together aspects of microfluidic and optical design, technology development and product design engineering to develop a new class of instrument for cell-based picodroplet discovery. We are delighted to have been awarded the KTP Fellowship and to welcome Dr. John McGrath to the Sphere Fluidics team. The work demonstrates the importance of collaboration between academic and industrial partners to support the advancement of novel microfluidic technologies for ground-breaking research.”
Dr. John McGrath, Research Scientist at Sphere Fluidics, commented: “I am thrilled to be working alongside commercial and academic leaders in the research and development of microfluidic instruments and technology. The ease of use and broader application set of the instrument to be developed in this project should lower the barrier to entry for a wide number of scientists, who are focused upon high-throughput screening, synthetic biology, gene editing, and antimicrobial resistance workflows. The technology has the potential to be a key driver in increasing the uptake of picodroplet microfluidic instruments.”

Precision cancer medicine requires personalized biomarkers to identify patients who will benefit from specific cancer therapies. In an effort to improve the accuracy of predictions about prognosis for patients with breast cancer and the efficacy of personalized therapy, University of North Carolina Lineberger Comprehensive Cancer Center researchers have developed a method to precisely identify individual patients who have aggressive breast cancer. The new approach involves sorting and characterizing invasive breast cancer cells by epigenetic characteristics – a method that involves analysing how particular regulatory proteins interact with DNA to control their expression – as well as by how the genes are amplified or abnormally expressed. The researchers reported that they used this technique to identify potential new prognostic markers to predict distinct clinical outcomes for two major subtypes of breast cancer.

“This paper describes a ground-breaking multi-omics technology to discover drivers of proliferative and invasive breast tumours,” said Xian Chen, PhD, professor in the UNC School of Medicine Department of Biochemistry & Biophysics. “We think that eventually, these tools could help doctors better predict which particular patients have a good response, or acquire resistance to treatment.” Doctors often rely on information about tumour size, whether the cancer has spread and the tumour subtype to make treatment decisions. In addition to clinical subtypes of breast cancer, researchers have discovered molecular subtypes that have been used to help make treatment decisions. However, Chen argues that existing markers do not adequately distinguish breast cancer patient sub-populations with different clinical outcomes.

“Single ‘omics’ approaches, which rely on either genomics, transcriptomics, or proteomics alone, fail to dissect the heterogeneity that contributes to individual patients’ variability in terms of their rates of tumour growth, metastasis, or susceptibility to anti-cancer therapies,” he said. “Because biomarkers are not available to distinguish distinct patient sub-populations that are either responsive or resistant to particular drugs, doctors do not have all the tools they need to predict patient response to treatment and outcomes.”
In their study, the researchers wanted to see if they could stratify patients beyond existing molecular subtypes. Their goal was to develop a method to determine which patients within a single subtype would develop resistance or invasive cancer. There are five major molecular subtypes of breast cancer, which are classified based on how genes are expressed in a tumour.

Chen and his colleagues analysed luminal breast cancer and basal-like breast cancer, which is more commonly known as triple negative breast cancer, using breast cancer samples from two large international studies, The Cancer Genome Atlas and the Molecular Taxonomy of Breast Cancer International Consortium.

To move beyond subtype for identifying exactly which patients might develop resistance, they first sorted the most invasive tumour cells in frozen tissue using a molecular probe that was able to distinguish tumour from adjacent non-malignant cells or tissue by binding to an epigenetic regulator, or a histone methylase, called G9a. This enzyme has been reported by other scientists to be abnormally upregulated in many cancer types, including breast cancer.

They then identified select proteins that were working with G9a as partners-in-crime, and worked backwards from there to identify the genetic abnormalities linked to those partner proteins in the cancer cell. They found in many instances the genes for these interactor proteins were amplified in multiple copies, or abnormally overexpressed, rather than mutated.

“Nowadays, people think somatic mutations of select genes are the primary drivers of tumorigenesis,” Chen said. “We didn’t see many mutations on our identified driver genes. We actually found the genes encoding those interactors have a high frequency amplification in breast cancer patients with poor prognosis.”

They then used this information to generate sets of genes that encoded these “interactor proteins,” and identified those linked to poor prognosis in patients. Looking ahead, Chen and his colleagues plan to determine the specificity and sensitivity of multi-omic aberrations of particular interactor gene sets as new systems biomarkers to predict cancer patient prognosis.

University of Northern Carolina

www.med.unc.edu/biochem/news/

by Dr Carsten Lange
Flow cytometry is a powerful technique for the detailed analysis of complex populations which, over the last two decades, has evolved from a staple technique of the research laboratory into an essential part of the modern clinical laboratory.
Some of the current ‘norms’ for clinical flow cytometry include its critical use for phenotyping hematological malignancies, as well as playing a vital role – along with other testing methods – in diagnosing disease, informing treatment plans and monitoring patients. Only time can tell how this powerful analytical technology will contribute to the clinical lab of the future. We can, however, anticipate that it will only continue to increase in importance, based on technical innovations that have driven the evolution of flow cytometry over the last decade.
In addition to today’s applications in disease diagnostics, the power of this technology continues to be used in cell biology research and pharmaceutical discovery. This evolution has been made possible by a higher number of analytical parameters to measure cells in suspension. The first cytometers were systems capable of merely three or four parameters, using a single laser and four detectors, and were the size of a small car. Today, however, flow cytometers (including cell sorters) can analyse more than 30 parameters, and new technology in benchtop analysers can deliver exponentially better performance in a smaller footprint.
Shifting paradigms
This paradigm shift, toward higher performance in a small instrument, is driven by clinical laboratories that want to capture the power of flow cytometric analysis, but don’t want to invest a significant amount of time in learning the instrumentation. The democratization of flow cytometry is enabled by key advances in technology. Advantage is being taken of prominent concepts in other scientific fields, such as the telecommunications industry, to allow the subsystems to be miniaturized while at the same time providing even better performance. These compact high-performance systems not only deliver better performance than historically expensive systems, but they are also easy to set up, operate and maintain, enabling a greater number of clinical laboratories to maximize the power of flow cytometry.
The power to see more
Performance of flow cytometers is typically measured by their capacity to resolve and their sensitivity to detect dim and/or rare populations. In this regard, efficient light management for optimal excitation and emission of fluorochrome-tagged cells is critical to performance.
With conventional flow cytometers, laser excitation sources are optimized by shaping and focusing light through a series of lenses and filters onto a flow cell where cells are hydrodynamically focused. However, newer flow cytometers use unique laser designs that are focused onto a flow cell with integrated optics. These systems can ensure increased excitation of the dyes not only on (and within) cells, but also increased collection of the emitted light for integration and measurement. When designing a compact clinical cytometer, the use of fibre optics to carry light is an efficient way of transmission, providing flexibility in laying out system components. These cables capture emitted light to deliver it onto a unique detector array, reducing crosstalk between channels, which improves performance.
Another recent development is a key concept borrowed from the telecommunications industry, the wavelength division multiplexer (WDM), which is used for light detection and measurement. Wavelength division multiplexing is a method used to deconstruct and measure multiple wavelengths of light as signals that relate to analytical parameters. The detectors used to measure each parameter are avalanche photodiodes (APDs), which are highly sensitive semiconductor devices. By contrast, conventional clinical cytometers to date have (and continue to use) photomultiplier tubes (PMTs). The major advantages of using APDs over PMTs include but are not limited to:

1. enhanced linearity;
2. 4–5 times the quantum efficiency;
3. higher dynamic range, 106 versus 103;
4. smaller size and about one-tenth the cost.

Shows the WDM of the first commercially available clinical cytometer to use compact APDs which reduce the overall instrument footprint (DxFLEX, Beckman Coulter). Each WDM contains optical and detector components to selectively measure specific wavelengths. This improves light collection for higher sensitivity to detect dim populations.
The WDM’s innovative and simple design uses a single bandpass filter to select the various colours of light. This contrasts with traditional clinical cytometers, which use a series of dichroic steering filters and bandpass filters that bounce the light along an array, leading to successively less available light, resulting in diminishing light collection efficiency, and ultimately compromising fluorescence sensitivity and resolution.
Simplifying high complexity
Leveraging the linearity of detection systems that use APDs in the operation of the cytometer can be dramatically simplified owing to the predictability of the signals. The linear gain and the normalization performed during the daily quality control routine takes care of the relative variations during instrument set-up commonly seen in instruments. Further, setting up a highcomplexity assay is simplified by using a software gain-only adjustment. The linearity of gain adjustment also simplifies the typically arduous task of spectral compensation which has been the barrier for many to push to a higher number of colours/ parameters. To maximize the benefit of the APD linearity, new software algorithms have been developed that facilitate set-up and analysis of high-complexity experiments by simplifying compensation.
It is now possible to create a compensation library that stores the APD gain settings and spectral spill-over coefficients for every parameter and multicolour combination. This allows users to make a virtual spectral compensation matrix selecting various single colours from the library. In addition, the library can intelligently adjust the compensation values when gains are adjusted owing to the predictive responses of linear APDs. The result is a dramatically simplified and intuitive method of setting up high-complexity applications.
The size factor
For most cytometers, measuring size of particles less than 300|nm is difficult because they deliver relative sizing information using forward scattered light from the 488|nm blue laser. For these systems, particles of less than 1|mm (1000|nm) usually fall below the noise threshold of the laser and detector subsystems. In contrast, newer systems use principles of Mie scattering, which predicts that with lower wavelengths of excitation there will be an increased amount of scattered light and improved resolution.
Therefore, measuring scattered light from a shorter-wavelength 405|nm violet laser versus a longer-wavelength 488|nm blue laser will allow the system to resolve smaller particles. The use of the violet side scatter parameter enables systems to detect particles of less than 0.2|mm (200|nm) in size, enabling excellent resolution of microparticles.
The future is now
Combining powerful performance and innovative design and technology, it is possible to deliver a compact, easy-to-use flow cytometer. Pushing the ‘norms’ of conventional flow cytometry, today’s – and tomorrow’s – cytometers simplify high-complexity applications in the clinical laboratory, as well as a deeper understanding in the frontier applications of hematopoietic cancers. Flow cytometry remains a powerful tool for interrogating complex questions. Today’s clinical laboratories want to harness that power and are demanding smaller and more powerful flow cytometers that are more affordable and easier to use. Using innovation, engineers can deliver solutions to meet the challenge.
The author
Carsten Lange PhD
Beckman Coulter GmbH, 47807 Krefeld, Germany

E-mail: clange@beckman.com DxFLEX flow cytometer: https://www.mybeckman.uk/flow-cytometry/instruments/dxflex

A new type of blood test for breast cancer could help avoid thousands of unnecessary surgeries and otherwise precisely monitor disease progression, according to a study led by the Translational Genomics Research Institute (TGen) and Mayo Clinic in Arizona.

The study suggests that the test called TARDIS — TARgeted DIgital Sequencing — is as much as 100 times more sensitive than other blood-based cancer monitoring tests.

TARDIS is a “liquid biopsy” that specifically identifies and quantifies small fragments of cancer DNA circulating in the patient’s bloodstream, known as circulating tumour DNA (ctDNA). According to the study, TARDIS detected ctDNA in as low as 2 parts per 100,000 in patient blood.
“By precisely measuring ctDNA, this test can detect the presence of residual cancer, and inform physicians if cancer has been successfully eradicated by treatment,” said Muhammed Murtaza, M.B.B.S., Ph.D., Assistant Professor and Co-Director of TGen’s Center for Noninvasive Diagnostics. He also holds a joint appointment on the Research Faculty at Mayo Clinic in Arizona, and is one of the study’s senior authors.

For example, Dr. Murtaza explained, TARDIS is precise enough to tell if early stage breast cancer patients have responded well to pre-operative drug therapy. It is more sensitive than the current method of determining response to drug therapy using imaging.

“This has enormous implications for women with breast cancer. This test could help plan the timing and extent of surgical resection and radiation therapy after patients have received pre-operative therapy,” said Dr. Barbara A. Pockaj, M.D., a surgical oncologist who specializes in breast and melanoma cancer patients at Mayo Clinic in Arizona, and is the study’s other senior author. Dr. Pockaj is the Michael M. Eisenberg professor of surgery and the chair of the Breast Cancer Interest Group (BIG), a collaboration between researchers at Mayo, TGen and ASU.

Unlike traditional biopsies, which only produce results from one place at one time, liquid biopsies use a simple blood draw, and so could safely be performed repeatedly, as often as needed, to detect a patient’s disease status.

“TARDIS is a game changer for response monitoring and residual disease detection in early breast cancer treated with curative intent. The sensitivity and specificity of patient-specific TARDIS panels will allow us to tell very early, probably after one cycle, whether neo-adjuvant (before surgery) therapy is working and will also enable detecting micro-metastatic disease and risk-adapted treatment after completing neo-adjuvant therapy,” said Dr. Caldas, who also is Senior Group Leader at the Cancer Research UK Cambridge Institute, and one of the study’s contributing authors.

Following further clinical testing and trials, TARDIS could someday be routinely used for monitoring patients during cancer treatment, and discovering when patients are essentially cured and cancer free.

“The results of these tests could be used to individualize cancer therapy avoiding overtreatment in some cases and under treatment in others,” Dr. Murtaza said. “The central premise of our research is whether we can develop a blood test that can tell patients who have been completely cured apart from patients who have residual disease. We wondered whether we can see clearance of ctDNA from blood in patients who respond well to pre-surgical treatment.”

Current tests and imaging lack the sensitivity needed to make this determination.

“Fragments of ctDNA shed into blood by tumours carry the same cancer-specific mutations as the tumour cells, giving us a way to measure the tumour,” said Bradon McDonald, a computational scientist in Dr. Murtaza’s lab, and the study’s first author.

“The problem is that ctDNA levels can be so low in non-metastatic cancer patients, there are often just not enough fragments of ctDNA in a single blood sample to reliably detect any one mutation. This is especially true in the residual disease setting, when there is no obvious tumour left during or after treatment,” McDonald said. “So, instead of focusing on a single mutation from every patient, we decided to integrate the results of dozens of mutations from each patient.”
Translational Genomics Research Institutewww.tgen.org/news/2019/august/07/new-ctdna-blood-test-for-cancer/

Researchers at Karolinska Institutet have developed a method based on artificial intelligence for histopathological diagnosis and grading of prostate cancer. The AI-system has the potential to solve one of the bottlenecks in today’s prostate cancer histopathology by providing more accurate diagnosis and better treatment decisions. The study shows that the AI-system is as good at identifying and grading prostate cancer as world-leading uro-pathologists.
“Our results show that it is possible to train an AI-system to detect and grade prostate cancer on the same level as leading experts,” says Martin Eklund, associate professor at the Department of Medical Epidemiology and Biostatistics at Karolinska Institutet who led the study. “This has the potential to significantly reduce the workload of uro-pathologists and allow them to focus on the most difficult cases.”
A problem in today’s prostate pathology is that there is a certain degree of subjectivity in the assessments of the biopsies. Different pathologists can reach different conclusions even though they are studying the same samples. This leads to a clinical problem where the doctors must pick treatment based on ambiguous information. In this context, the researchers see significant potential to use the AI-technology to increase the reproducibility of the pathological assessments.
To train and test the AI system, the researchers digitized more than 8000 biopsies taken from some 1200 Swedish men in the ages of 50–69 to high-resolution images using digital pathology scanners. About 6,600 of the samples were used to train the AI system to spot the difference between biopsies with or without cancer. The remaining samples, and additional sets of samples collected from other labs, were used to test the AI system. Its results were also compared against the assessments of 23 world-leading uro-pathologists. The study was conducted in collaboration with researchers at Tampere University in Finland.
The findings showed that the AI-system was almost near-perfect in determining whether a sample contained cancer or not, as well as in estimating the length of the cancer tumour in the biopsy. When it comes to determining the severity of the prostate cancer, the so-called Gleason score, the AI system was on par with the international experts.
“When it comes to grading the severity of the prostate cancer, the AI is in the same range as international experts, which is very impressive, and when it comes to diagnostics, to determine whether or not it is cancer, the AI is simply outstanding,” says Lars Egevad, professor in pathology at Karolinska Institutet and co-author of the study.
The initial findings are promising but more validation is needed before the AI system may be rolled out broadly in clinical practice, according to the researchers. That is why a multicenter study spanning nine European countries is currently underway with completion slated by the end of 2020. That study, which is financed by EIT Health, aims to train the AI-system to recognize cancer in biopsies taken from different laboratories, with different types of digital scanners and with very rare growth patterns. In addition, a randomized study starting in 2020 will examine how the AI-model may be implemented in Sweden’s health care system.
“AI-based evaluation of prostate cancer biopsies could revolutionize future health care,” says Henrik Grönberg, professor in cancer epidemiology at Karolinska Institutet and head of the Prostate Cancer Center at St Göran Hospital in Stockholm. “It has the potential to improve the diagnostic quality, and thereby secure a more equitable care at a lower cost.” Karolinska Institute