The Measure of a Cell: How Advances in Technology are Reshaping Cytometry - Cytometry Now

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Community submitted commentary on the field of cytometry. We will feature blog posts from key leaders in the field discussing the direction of cytometry research, the impact of the latest findings and application of cytometry, and where the future of cytometry is headed.

The Measure of a Cell: How Advances in Technology are Reshaping Cytometry

The capacity for performing high dimensional analysis of single cells has drastically shaped our view of cells and tissues, revealing in-depth information on the molecular and cellular processes that correlate with physiology and pathology. The classical method of quantitative single cell analysis using light scatter and multiple fluorescent markers to quantitate cell components, cell physiology or cell phenotypes is flow cytometry (Büscher M). In recent years flow cytometry has dramatically expanded its technological capabilities allowing the simultaneous quantification a multitude of cell types labelled with multiple markers to characterize complete cell systems. Combining imaging with flow cytometry allows one to visualize the cellular location of fluorescently labelled proteins and other components in a cell with a technique termed imaging flow cytometry (Lei et al.). Adherent cultured cells and cells whole tissue sections can be quantitatively and cytometrically analyzed to generate a wealth of data through a blend of image cytometry and imaging mass cytometry (Goltsev et al.). As with all high-content analysis approaches to biological material, rigorous control of the measurement methodology but also the pre- and post-analytical process are essential to obtain reproducible data and results. Lack of reproducibility was already a challenge for gene expression arrays in the early 2000’s, initiating the development of bioscience reporting guidelines and tools, the MIBBI (Minimum Information for Biological and Biomedical Investigations; https://fairsharing.org/collection/MIBBI ) initiative, and is an ongoing topic of discussion in clinical and biological cell analysis studies.

Substantial achievements have been made by the cellular analytical community since that time to develop consensus-based methodology, protocols, and data validation protocols (Selliah et al). Further, the installment of mutually agreed upon guidelines for research documentation emphasizing transparency and accessibility of experimental protocols and promoting the open sharing of original data and analytical software developments have been key advances. Critical features of these guidelines include efforts to enforce standardization of analytical instrumentation (such as flow cytometers and microscopes), high levels of standardization, quality control, sample processing and storage, and methods of data analysis and presentation (Galli et al.).

One of the major sources of technical advancement in cytometry has been the implementation of multiple lasers with emission spectra that range from ultraviolet (UV) to infrared (IR) in flow and image cytometry instruments. Combined with the ability to utilize multiple detectors in a single instrument, it has greatly expanded the number of measurable colors and parameters in a single run to 40-plus indicators, a number that is still actively expanding. Flow cytometry is not limited to the use of fluorescent indicators however. Replacing the fluorescent indicators that tag each detecting antibody with metal isotopes allows for detection of cells with time-of-flight mass spectrometry, resulting in a technique termed Mass Cytometry (Bandyopadhyay et al.) or Imaging Mass Cytometry (Wang et al.). Approaches such as these have increased the dimensionality of the obtained data substantially beyond the level that can be obtained with fluorescent indicators (Galli et al.) but cytometry using fluorescent indicators is catching up.

The visualization and analysis of low (up to 8-10 markers) dimensional cytometry data is the standard methodology in clinical studies and diagnosis (Carrion et al., Gambella et al.); however, their currently used manual analysis methods are challenging to reproduce. When biological samples such as sectioned tissue biopsies, which only give limited information using standard clinical approaches like histopathological analysis by an expert, are analyzed by state of the art, high-dimensional imaging cytometry methods the number of measurable parameters per cell increases drastically to provide a comprehensive set of morphological, spatial and stereological data of the cells contained within the tissue (Goltsev et al., Wang et al.).

With increasing complexity of data, comes the need for increasingly complex ways to interpret that data. Sophisticated analytical software programs are integral parts of the observer’s toolbox, allowing them to process, visualize and interpret dimensionally complex data sets. Analytical methods including SPADE (Spanning-Tree Progression Analysis of Density-normalized Events) and SNE (Stochastic Neighbor Embedding), improved data visualization by reducing the dimensions of a measured data set from 20-plus down to two to make it more accessible (we recommend the review by Galli et al. 2019 on recent developments in analytical tools for high-dimensional data sets). The development of computational biology approaches for cytometry is ongoing, with tools such as UMAP (Uniform Manifold Approximation and Projection; Becht et al.) allowing for non-linear reduction in dimensions and robust organization of cellular data results. Data visualization in combination with automated learning algorithms like machine learning and deep learning are poised to provide important tools to identify critical and meaningful differences between test and patient groups in clinical orientated studies (Galli et al., Goltsev et al., Lei et al.).

Recent achievements and discoveries made by high-dimensional cytometry illustrate the power and perspective of this innovative technology. The Feuillard laboratory (Carrion et al.) reported the development of a nine color immunophenotyping panel that comprehensively characterizes B-cell development in human bone marrow and blood. This panel can provide reference values for studying and diagnosing B-cell related abnormalities. In Bandyopadhyay et al. (2019), the researchers applied mass cytometry and high dimensional data analysis to examine cell populations in blood samples from patients with secondary acute myeloid leukemia (sAML). They report distinct phenotypic differences and aberrant hematopoietic stem cell populations in sAML patients as compared to healthy control subjects with a relevant subset in sAML patients displaying a Lin/CD61+/CD34/CD38/CD45low phenotype. Yang et al. (2019) conducted a comprehensive examination of T-cell populations from follicular lymphoma (FL) patients, a disease marked by tumors generated by B cell malignancy. Using mass cytometry analysis of leukocytes isolated from human spleen, lymph node and tonsils they isolated a dozen distinct T-cell subsets present in tumors of FL patients. Importantly, the authors identified CD27 and CD28 low and high subsets of CD4+PD-1+ T-cells. Their cell counts were correlated with patient’s survival and may result in a new diagnostic assay in the future. Gambella et al. used 6 or 8 color flow cytometry and PCR to detect minimal residual disease (MRD) in patients with multiple myeloma and mapped the cell population results to their progression-free survival. They discovered that MRD status serves as a stronger predictor of progression-free survival of multiple myeloma patients than the current predictor method that relies on standard risk factors.

Two recent publications, Goltsev et al (2019) and Wang et al (2019), serve as excellent examples to illustrate the discovery power of high-dimensional image cytometry. Goltsev et al. describe a novel protocol termed CODEX (CO-Detection by indexing), a high dimensional tissue cytometry assay, to examine the differences between spleen tissue taken from control mice and mice with a systemic autoimmune disease. The authors generated multiplexed data sets by sequential oligonucleotide extension using fluorescently conjugated nucleotides, imaging and removal of the fluorophores to generate barcodes to mark specific cell populations. Although this method is substantially more time intense than current high-dimensional flow cytometry, this level of analysis of tissue architecture can only be achieved by imaging cytometry and the CODEX method presented by the authors provides a means to gain critical new information of tissue cell populations. Using CODEX, they report that the surface-marker expression of cell populations depends on the local microenvironment. Further, that changes to splenic composition are associated with disease progression, allowing for characterization of cellular niches during systemic autoimmune disease pathology. In Wang et al (2019), a combinatorial approach of imaging mass cytometry was used to evaluate cellular populations from human pancreas biopsies of patients clinically diagnosed with type 1 diabetes (T1D). They discovered that during T1D progression, pancreatic islet structural changes occurred along with alterations to endocrine and immune cell composition. This quantitative and comprehensive histological approach can generate new and improved cut-off values for diagnosis, such as for insulitis which is based on CD45+ cell counts within a pancreatic islet. To verify combinatorial approaches of cytometric single cell phenotyping, the use of mRNA signature analysis has potential to be a useful independent tool (Monaco et al 2019).

The discoveries such as those discussed above obtained by high-dimension cell analytical approaches, when combined with sophisticated bioinformatic analysis, help to unravel key cellular players in disease and can explain heterogeneities between patients. Patients, even those suffering from the same clinically diagnosed disease, can respond different to established therapies and therefore present striking differences in their prognosis. Extraction of the key cell types and their unequivocal identification by flow or image cytometry will lead to a revolution in treatment, where a patient will be diagnosed using improved disease-specific analytical tools, followed by individualized therapy selection and progression monitoring.

 

References

Büscher M. Flow Cytometry Instrumentation – An Overview. Curr Protoc Cytom. 2019 Jan;87(1):e52.

Selliah N, Eck S, Green C, Oldaker T, Stewart J, Vitaliti A, Litwin V. Flow Cytometry Method Validation Protocols. Curr Protoc Cytom. 2019 Jan;87(1):e53. doi: 10.1002/cpcy.53.

Carrion C, Guérin E, Gachard N, le Guyader A, Giraut S, Feuillard J. Adult Bone Marrow Three-Dimensional Phenotypic Landscape of B-Cell Differentiation. Cytometry B Clin Cytom. 2019 Jan;96(1):30-38.

Bandyopadhyay S, Fowles JS, Yu L, Fisher DAC, Oh ST. Identification of functionally primitive and immunophenotypically distinct subpopulations in secondary acute myeloid leukemia by mass cytometry. Cytometry B Clin Cytom. 2019 Jan;96(1):46-56.

Yang ZZ, Kim HJ, Villasboas JC, Price-Troska T, Jalali S, Wu H, Luchtel RA, Polley MC, Novak AJ, Ansell SM. Mass Cytometry Analysis Reveals that Specific Intratumoral CD4+ T Cell Subsets Correlate with Patient Survival in Follicular Lymphoma. Cell Rep. 2019 Feb 19;26(8):2178-2193

Monaco G, Lee B, Xu W, Mustafah S, Hwang YY, Carré C, Burdin N, Visan L, Ceccarelli M, Poidinger M, Zippelius A, Pedro de Magalhães J, Larbi A. RNA-Seq Signatures Normalized by mRNA Abundance Allow Absolute Deconvolution of Human Immune Cell Types. Cell Rep. 2019 Feb 5;26(6):1627-1640.

Goltsev Y, Samusik N, Kennedy-Darling J, Bhate S, Hale M, Vazquez G, Black S, Nolan GP. Deep Profiling of Mouse Splenic Architecture with CODEX Multiplexed Imaging. Cell. 2018 Aug 9;174(4):968-981.

Wang YJ, Traum D, Schug J, Gao L, Liu C; HPAP Consortium, Atkinson MA, Powers AC, Feldman MD, Naji A, Chang KM, Kaestner KH. Multiplexed In Situ Imaging Mass Cytometry Analysis of the Human Endocrine Pancreas and Immune System in Type 1 Diabetes. Cell Metab. 2019 Jan 28. pii: S1550-4131(19)30003-8.

Becht E, McInnes L, Healy J, Dutertre CA, Kwok IWH, Ng LG, Ginhoux F, Newell EW. Dimensionality reduction for visualizing single-cell data using UMAP. Nat Biotechnol. 2018 Dec 3. doi: 10.1038/nbt.4314. [Epub ahead of print]

Gambella M, Omedé P, Spada S, Muccio VE, Gilestro M, Saraci E, Grammatico S, Larocca A, Conticello C, Bernardini A, Gamberi B, Troia R, Liberati AM, Offidani M, Rocci A, Palumbo A, Cavo M, Sonneveld P, Boccadoro M, Oliva S. Minimal residual disease by flow cytometry and allelic-specific oligonucleotide real-time quantitative polymerase chain reaction in patients with myeloma receiving lenalidomide maintenance: A pooled analysis. Cancer. 2019 Mar;125(5):750-760.

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Lei C, Kobayashi H, Wu Y, Li M, Isozaki A, Yasumoto A, Mikami H, Ito T, Nitta N, Sugimura T, Yamada M, Yatomi Y, Di Carlo D, Ozeki Y, Goda K. High-throughput imaging flow cytometry by optofluidic time-stretch microscopy. Nat Protoc. 2018 Jul;13(7):1603-1631.