WO2007010236A1 - Analyse de donnees en cytometrie de flux automatique - Google Patents

Analyse de donnees en cytometrie de flux automatique Download PDF

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WO2007010236A1
WO2007010236A1 PCT/GB2006/002655 GB2006002655W WO2007010236A1 WO 2007010236 A1 WO2007010236 A1 WO 2007010236A1 GB 2006002655 W GB2006002655 W GB 2006002655W WO 2007010236 A1 WO2007010236 A1 WO 2007010236A1
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data
points
cells
cumulative probability
data points
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Timothy James Coffey
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MATHSHOP Ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/10—Investigating individual particles
    • G01N15/14—Optical investigation techniques, e.g. flow cytometry
    • G01N15/1468—Optical investigation techniques, e.g. flow cytometry with spatial resolution of the texture or inner structure of the particle
    • G01N15/147—Optical investigation techniques, e.g. flow cytometry with spatial resolution of the texture or inner structure of the particle the analysis being performed on a sample stream
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/10—Investigating individual particles
    • G01N15/14—Optical investigation techniques, e.g. flow cytometry
    • G01N15/1429—Signal processing
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
    • G06F18/2135—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on approximation criteria, e.g. principal component analysis
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2413—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
    • G06F18/24133—Distances to prototypes
    • G06F18/24137—Distances to cluster centroïds
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/60—Type of objects
    • G06V20/69—Microscopic objects, e.g. biological cells or cellular parts
    • G06V20/698—Matching; Classification
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/10—Investigating individual particles
    • G01N15/14—Optical investigation techniques, e.g. flow cytometry
    • G01N2015/1402—Data analysis by thresholding or gating operations performed on the acquired signals or stored data
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/10—Investigating individual particles
    • G01N15/14—Optical investigation techniques, e.g. flow cytometry
    • G01N2015/1477—Multiparameters
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/10—Investigating individual particles
    • G01N15/14—Optical investigation techniques, e.g. flow cytometry
    • G01N2015/1486—Counting the particles
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/10—Investigating individual particles
    • G01N15/14—Optical investigation techniques, e.g. flow cytometry
    • G01N2015/1488—Methods for deciding
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume or surface-area of porous materials
    • G01N15/10—Investigating individual particles
    • G01N15/14—Optical investigation techniques, e.g. flow cytometry
    • G01N2015/1493—Particle size

Definitions

  • the invention relates to flow cytometry and, in particular, the automatic analysis of data derived from multiple flow cytometry samples.
  • Flow cytometry provides a method of simultaneously detecting and recording multiple characteristics of microscopic particles, usually cells, dispersed in a fluid stream by means of their interaction with a beam of laser light. Physical characteristics of cells, such as size, granularity and other internal structure, are measured by reference to the scattering of incident laser light, and the use of specific fluorochrome labelling allows detailed phenotypic characterisation based on the fluorescent response to the laser.
  • flow cytometers comprise a fluidics system to deliver a stream of single cells to a laser beam, an optical system of one or more laser sources together with optical filters and detectors to detect scattering and fluorescence, an electronic system to convert the resultant optical signals into electronic signals to record the data acquired in a suitable format for analysis, and processing apparatus and software to analyse and display the results in a useful way.
  • this analysis may be combined with subsequent sorting of subpopulations of cells, whereby cells of a particular combination of markers are physically separated by means of selectively imparting an electrical charge and electrostatically diverting selected cells.
  • FSC Forward scattered light
  • SSC Side scattered light
  • Side scatter is commonly used to differentiate, for instance, granular blood cells such as granulocytes, and the combination of forward and side scatter can be used to differentiate all the major types of, for example, blood cells by their physical characteristics. Dead cells and debris may also be differentiated.
  • the light scattering characteristics of samples rely heavily on the sample viability and preparation technique. This means that samples must be analysed promptly and that sample preparation must be consistent.
  • phenotypic characteristics requires labelling and the detection of fluorescence. This is most conveniently done by specific labelling of informative cell surface markers by binding antibodies, directly or indirectly labelled with fluorochromes.
  • a single (usually) monoclonal antibody known to label a particular subset of cells may be used. This may be directly labelled with one of a variety of fluorochromes, which fluoresce in response to the energy of the laser and emit a characteristic spectrum of light to which a detector within the apparatus responds.
  • the primary antibody may not itself be labelled but after incubation with, and binding to, the target cell, a secondary, antiimmunoglobulin antibody may be used indirectly to label the first.
  • fluorescent labels is fluoroscein isothiocyanate (FITC). It has an absorption maximum of about 490nm, close to the 488nm wavelength of the argon ion laser most commonly used in flow cytometers.
  • FITC fluoroscein isothiocyanate
  • Flow cytometry can also be used to analyse cells on the basis of specific internal staining. Internal markers may be labelled by first permeablising the cell before staining with antibody. This method is often used to identify cells with intracellular cytokines (Carter & Swain, 1997, Curr Opin Immunol 9: 177- 182). A number of DNA staining methods have been developed, which allow flow cytometric investigation of cell cycle and DNA content. Dyes such as propidium iodide and acridine orang ⁇ intercalate into nucleic acids and give characteristic and informative fluorescence. In some cases, delays in analysis may also lead to loss of labile markers or escape of intracellular markers, leading to inconsistent results.
  • Results may not always reflect what was happening in vivo because cells can change immune modulator expression during sample collection, transport and staining (Prussin & Metcalfe, 1995, J Immunol Meth 188: 117-128). These effects may be regulated by comparison with normal or control cells that have undergone the same collection, storage and preparation procedure. This however will not compensate for immune modulators that are lost during sample processing.
  • Flow cytometers are complex and consistent results can only be achieved by skilled operators.
  • the initial setting up of the machine parameters such as forward scatter, side scatter and colour compensation, plays a major role in the reproducibility of the results.
  • the compensation settings are liable to change due to daily variations and slight movements of the laser, these must be recorded so that any faults with the machine can be highlighted and dealt with. Drifts in the laser alignment that are not controlled can lead to false positive or negative results (Owens & Loken, 1995, Flow Cytometry Principles for Clinical Laboratory Practice, Wiley-Liss).
  • the fluorochromes FITC 1 phycoerythrin (PE) and peridinin chlorophyll protein (PerCP) all have suitable absorption spectra for use with a 488nm laser with sufficiently distinct emission spectra to be conveniently detected.
  • PE phycoerythrin
  • PerCP peridinin chlorophyll protein
  • Current flow cytometers may have three lasers (commonly diode-pumped solid state lasers of 407, 488 and 534nm) allowing eight-colour analysis (Roederer et al, 1997, Cytometry 29: 328-339) and recently development of a 17-colour system has been described, using an additional helium argon laser of 633nm (Perfetto et a/, 2004, Nature Reviews Immunology 4: 648-655).
  • light signals scattering and fluorescence
  • photodetectors which generate voltage pulses the size of which are proportional to the number of photons detected.
  • Such pulses may be amplified in a linear or logarithmic manner and then assigned a digital value by an analogue to digital converter.
  • a 0 to 100OmV pulse is assigned a digital number corresponding a 0 to 100OmV channel allowing subsequent display of the signal in the appropriate position on a data plot.
  • a detection threshold may be set on one or more parameters. Only signals of intensity equal to or greater than the detection threshold channel value will be processed and analysed. For example, commonly, when analysing blood cell samples, data from red blood cells and cell debris will be excluded by reference to their small size by setting a suitable FSC detection threshold.
  • FCS flow cytometry standard
  • SSC simple 4-parameter analysis
  • FL1 and FL2 two colours
  • FL1 and FL2 two colours
  • a typical sample may comprise 10 000 cells and hence 8OkB of data.
  • a gate is a numerical or graphical boundary that defines the characteristics of a group of cells specifically included for further analysis (or specifically excluded from it).
  • the first stage of analysis consists of displaying single suitable parameter, such as FSC 1 in a histogram plotting FSC (x) against counts (y); or plotting FSC (x) against SSC (y). Subpopulations may then be gated on the basis of size and/or granularity, which is often sufficient to select, for example, granulocytes or lymphocytes from a mixed population of blood cells.
  • Analysing cell populations by gating two parameters at a time is known as bivariate gating.
  • Two parameters may be conveniently displayed in so-called 'dot plots' and even complex, multivariate analysis is often derived from successive application of bivariate gating on different parameters.
  • Cells may be gated in two ways; by region, or by quadrants. Gating by region comprises drawing a gate around a pre-identified population or cluster of interest. This isolates cells within the region as a sub-population for further analysis or sorting, whilst excluding irrelevant cells.
  • Such regional gating is usually done by manually by an experienced operator, since it requires judgement as to the boundaries of the population selected. Where this is a clearly defined, isolated cluster of cells this may be straightforward. However, where the population of interest is more diffuse, or partially overlaps with another population, the selection may be difficult, leading to arbitrary and inconsistent gating.
  • Gating by quadrant divides two-parameter dot plots into four sections to distinguish populations that are considered negative, single-positive or double positive for the two parameters concerned. Although this amounts to setting a single value gate on each of the x and y axes, the selection still depends on the judgement of the operator and is somewhat subjective. It also fails to take account of clusters with a predominantly diagonal distribution and may result in either poor separation of such populations or gating that excludes a significant proportion of cells that ought to be included.
  • cluster analysis may be used in attempt to apply objective regional gating.
  • Cluster algorithms attempt statistically to identify subsets of data points (such as cells) with similar characteristics or degrees of difference (Bakker Schutt et al (1993) Cytometry 14: 649-659).
  • Boker Schutt et al (1993) Cytometry 14: 649-659 but such algorithms are inefficient in terms of processing time and do not take account of factors such as a given 'distance' in one region of multidimensional space being more significant than the same 'distance' in a different region.
  • probability binning uses a process known as adaptive binning to group events together into "bins”. Statistical operations are then performed on the bins rather than individual events. This is done in two stages. Firstly adaptive binning is used to divide events into hyperrectangular bins. Doing this correctly is crucial since the data must be divided sufficiently to ensure separation of distinct populations, but not so much as to undermine the computational gain of collecting similar events into single bins. The second stage involves joining immediately adjacent clusters, as long as the joining does not significantly change the distribution of any parameter relevant to the clustering.
  • US patent 6,897,954 discloses a cytometer set-up method comprising a method for indirect adjustment of photomultiplier gains as part of the pre-data acquisition set-up to minimise spill-over from multiple fluorochromes.
  • US patent application 2004/0143423 relates to a method of analysing flow cytometry data, particularly data representing a large dynamic range.
  • the method comprises scaling the low value range of the data linearly and the high value part of the data logarithmically.
  • Us application 2005/0118572 discloses a method for 'multiplexed' analysis of soluble biomolecules using binding to a labelled set of microsphere beads and analysis by flow cytometry with or without simultaneous analysis of multiple analytes in real time.
  • thresholds are set empirically by the operator, not automatically.
  • the invention provides a quantitative method of analysing flow cytometry data points by identifying clustered data points representing sub- populations of cells by reference to one or more selected parameters comprising: first,
  • step (A) points that are within the beyond the tolerance of instrument saturation (i.e. very bright or positive points at the upper limit of the instrument's response) are discarded. Where beads are used for volume calculation, such data points are eliminated in this way.
  • the threshold defined in step (A) may be an upper or lower threshold and that the logic test of step (B) may therefore identify and record a cell as being positive (above a lower threshold and/or below an upper threshold) or negative (below a lower threshold or above an upper threshold).
  • step (A) comprises automatically defining at least a lower threshold for each marker by reference to an applicable control tube.
  • the maximum value of the data in the control tube defines a lower threshold below which the applicable marker is considered negative.
  • rogue or outlier points mean that the absolute maximum value from the control tube is inappropriate and a statistical or alternative technique is required to automatically determine a more sensible threshold.
  • An example of such a technique for step (A) comprises automatically defining at least a lower threshold by means of;
  • the reference slope be based on values either side of the 50% cumulative probability; that is, on a lower value on the range 0 to 49.999% and an upper value in the range 50.001 to 100%.
  • the lower value is the range 0 to 20%, more preferably, 1 to 10%, most preferably approximately 10%.
  • the upper value is in the range 80 to 100%, more preferably approximately 90%.
  • step (iv) the remaining cumulative probability data is smoothed and scanned for the first point where the slope is greater than some small percentage of the reference slope. This percentage would normally be between 1 to 10%, but could be higher or lower.
  • the analysis is based on post-acquisition automatic setting of thresholds for each parameter used to define whether cells were deemed positive or negative for that parameter. That is, the analysis takes all acquired data for a particular sample, then, for each required parameter, automatically calculates and applies a threshold that defines the boundary of a population that is considered positive for that parameter. It rapidly, consistently and independently determines positive and negative populations by application of simple logic tests and then counts the populations so defined. This is of particular value when large numbers of samples are being analysed for the same markers, which would normally require frequent interventions from an operator.
  • the invention further provides a method wherein before the positive cells are counted in step (C), a further step of statistical elimination of outlying and/or aberrant data points is applied (steps (i) - (v), below) and points are counted to a similar degree of statistical significance (step vi).
  • a further step of statistical elimination of outlying and/or aberrant data points is applied (steps (i) - (v), below) and points are counted to a similar degree of statistical significance (step vi).
  • a further step of statistical elimination of outlying and/or aberrant data points is applied (steps (i) - (v), below) and points are counted to a similar degree of statistical significance (step vi).
  • a further step of statistical elimination of outlying and/or aberrant data points is applied (steps (i) - (v), below) and points are counted to a similar degree of statistical significance (step vi).
  • the invention further provides a quantitative method of analysing flow cytometry data points by identifying clustered data points representing sub- populations of cells by reference to two or more selected parameters comprising
  • step (xvii. repeating the process from step (v) until the mean of the selected points is not varying or the process has been repeated a set number of times
  • this method is applied where cells are analysed according to their forward and side light scattering characteristics. It is further preferred that steps (i) and/or step (v) further comprise the steps of defining an upper threshold by means of the addition of beads to the cell samples in order to establish the upper dynamic limit of the flow cytometer instrument and discarding data points that correspond to these beads. It is further preferred that the method comprises the step of quantifying cell concentration by reference to a known number of beads added to the samples.
  • the number, size and degree of overlap of the segments may vary according to circumstance. In principle, any number of segments may be used, but it is preferred that at 10 to 360 segments are used, preferably approximately 180. Similarly, the size and degree of overlap may be varied. Preferably, the segments are 1-180°, more preferably 1-45°, most preferably approximately 10°. Such segments may be placed every 1-180°, preferably every 1-45°, more preferably every 1-10°, most preferably approximately every 2°.
  • step xvii the number of iterations is determined by the circumstances. Preferably 3-10 repeats are used to obtain convergence, preferably approximately 5. However, as will appreciated by one of skill in the art, this somewhat arbitrary choice representing a compromise between precision and computing time.
  • the invention provides a computer program having one or more logic instructions for implementing the method herein described.
  • the invention also provides a computer program product comprising a computer- readable medium encoding one or more logic instructions for implementing any of the above-described methods and a recordal or storage medium on which such a program has been recorded.
  • the invention further provides an apparatus comprising a flow cytometer, either comprising or operably-connected to, a computer programmed to control the flow cytometer and/or to perform any of the above-described methods of gating or analysis, or to operate the above-mention program or program product.
  • the invention provides a system for analysing the phenotypic characteristics of cells by flow cytometry comprising;
  • A at least one flow cytometer
  • B at least one computer operably-connected to said flow cytometer, said computer having system software comprising one or more logic instructions for implementing any of the above-described methods.
  • Figure 1 shows the selection of monocytes as CD14-positive cells clustered according to their forward and side-scattering properties according to the method of Example 1.
  • the paler grey points represent positive selected data points enclosed by the elliptical selection boundary.
  • the darker points represent positive, but unselected data points.
  • the straight diagonal line represents the principal axis.
  • Figure 2 shows the selection of unlabelled granulocytes based on their forward and side-scattering properties according to the method of Example 2.
  • the paler grey points represent data selected for analysis, enclosed by the irregular analysis boundary.
  • the darker points represent raw data lying beyond the analysis boundary.
  • Control tubes are used to establish a lower cut off or threshold for each marker. It will be appreciated that the values are illustrative only and are not intended to limit the scope of the invention.
  • the steps used to determine the threshold for each marker are:
  • the curve of cumulative probability of a particular marker value is formed from the data.
  • This curve is typically S shaped and a reference slope may be determined based on the values for approximately 10% and 90% cumulative probability. 3. The cumulative probability curve is then scanned starting from 100% probability for the first reasonable run of significant data.
  • the remaining cumulative probability data, smoothed, is scanned for the first point where the slope is, for example, 5% of the reference slope. The point where this occurs is taken as the threshold.
  • the threshold is taken as the end of continuous values recorded with the control tube.
  • Beads are used to measure the instrument's flow rate and so it is necessary to identify them and to count them and also to ensure they are not included as cells.
  • Beads manifest themselves with very high values of side scatter and fluorescence and these characteristics are used to identify them.
  • the thresholds used to indicate very high values of side scatter and fluorescence are determined from the histograms and cumulative probabilities. When beads are present, the histograms have a peak very near the maximum value. This peak is detected and the threshold set just prior to it. Note that this peak and the maximum recorded values were not always the extremes of the scale.
  • Beads were taken as those whose recorded values exceeded the threshold values for side scatter and fluorescence on all markers that were detecting beads.
  • any remaining records whose side scatter or forward scatter values were at the maximum or minimum recorded were discarded as being off scale.
  • the general method is applicable to flow cytometry of a wide range of cells or other particles, whether fluorescently labelled or not, the principles may be illustrated by reference to two common applications involving analysis of a large number of samples using a common set of markers; firstly cells labelled with one or more fluorochrome-tagged monoclonal antibodies specific for informative cell surface structures, and secondly unlabelled cells being analysed on the basis of their optical scattering characteristics.
  • a positive conrtol sample comprises cells known to express high levels of a given cell surface structure, labelled with a fluorochrome-tagged antibody, preferably a monoclonal antibody of a high binding affinity.
  • a negative control comprises cells incubated with a a similarly labelled antibody known not to bind specifically to a cell surface structure. A low level of non-specific binding is expected and the level of non-specific labelleing resulting is used as a comparison to distinguish meaningful levels of specific binding.
  • T helper lymphocytes might be identified as CD3 positive AND CD4 positive.
  • Activated T helper cells might be defined as fulfilling the conditions CD3 positive AND CD4 positive AND CD25 positive.
  • multiparametric analysis of mixed cell populations is well-known in the art and well within the knowledge of one of appropriate skill. 4. If the distribution of the population is irregular a method to eliminate statistical outliers, as described in relation to unlabelled cells and claimed below, is applied.
  • the method may comprise the following.
  • segment by segment identification of the bounds of the population (by any suitable statistical or step change-based method).
  • Example 1 Selection of Lymphocytes or Monocytes
  • the steps used to select the cells of interest are:
  • the data for all cells meeting the criteria are selected. For example, in tube 1 all cells positive with marker CD3 and positive with marker CD4 are selected.
  • An axis system is placed in the data with origin at the mean so that the ratio of standard deviations ⁇ y / ⁇ x is minimised or ⁇ x / ⁇ y is maximised.
  • the x-axis in this system is referred to as the principal axis.
  • the result is a set of data that is somewhat comet-shaped.
  • the data are transformed into a circle by scaling the positive and negative x values and all y values by their respective standard deviations relative to the total sample means.
  • Steps 2 to 4 are repeated up to five times to ensure that points rejected had not overly biased the determination of the mean. Experience shows the process typically converges in two or three steps.
  • Steps 2 to 4 are repeated, but with the circle of 99% probability being used.
  • FIG. 1 A typical case is illustrated in Figure 1 based on FSC and SSC profile of CD 14- positive cells.
  • markers are not used to identify the cells of interest and the cells must be selected by identifying the region containing them on the FS SS chart.
  • the anti-CD 13 antibody used as one of the parameters was not binding to the granulocyte population with high enough affinity to produce the required amount of fluorescence to separate the stained cells from the unstained population. For this reason the CD 13 data was discarded and the granulocytes were distinguished by forward and side scatter characteristics alone, as follows.
  • the steps used to select the cells of interest are:
  • an axis system is placed in the data with origin at the mean so that the ratio of standard deviations ⁇ y / ⁇ x is minimised or ⁇ x / ⁇ y is maximised.
  • the x-axis in this system is referred to as the principal axis.
  • the data is transformed into this axis system, and the cumulative probability distribution of the x values formed.
  • the cumulative probability distribution of the x values is scanned from the maximum x value for the locations of the first and second maximum and the intervening minimum of the first derivative of the cumulative probability. If all three are found, the points with x greater than the location of the intervening minimum are taken as the first pass granulocytes.
  • the concentration of points that is right most when the data is aligned with its principal axis is taken as a first pass selection of the granulocytes.
  • the data in each segment is formed into a cumulative probability of radial distance from the mean, adjusted for the narrowing of the segment towards the mean.
  • the points of radial distance greater than the intervening minimum are taken as being not granulocytes, provided there are a significant number of cells to be discarded.
  • step 11 The process is repeated from step 4 until the mean of the granulocytes is not varying or the process has been repeated five times.
  • cells in the main region at the top right of the FS SS chart are selected as granulocytes.

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Abstract

Cytométrie de flux et analyse automatique des données dérivées de plusieurs échantillons de cytométrie de flux. Le procédé décrit est le suivant : identification de points de données en agrégats projetés en représentation 2D de propriétés phénotypiques spécifiques de cellules individuelles, y compris la détermination automatique de seuils pour définir les limites de ces agrégats dans le cadre d'une analyse de données post-acquisition. Se prête particulièrement à l'analyse automatique rapide d'un grand nombre d'échantillons de cellules. Egalement, programme informatique, dispositif et système pour la mise en oeuvre du procédé.
PCT/GB2006/002655 2005-07-18 2006-07-17 Analyse de donnees en cytometrie de flux automatique Ceased WO2007010236A1 (fr)

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Cited By (7)

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WO2015039425A1 (fr) * 2013-09-18 2015-03-26 深圳迈瑞生物医疗电子股份有限公司 Dispositif et procédé pour tester le sexe d'un échantillon, et procédé de détermination de standard de jugement
WO2015154288A1 (fr) * 2014-04-10 2015-10-15 深圳迈瑞生物医疗电子股份有限公司 Procédé de compensation automatique, dispositif et cytomètre de flux correspondant
WO2021154579A1 (fr) * 2020-01-31 2021-08-05 Becton, Dickinson And Company Procédés et systèmes pour ajuster une grille d'apprentissage pour recevoir des données de cytomètre en flux
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CN118520317A (zh) * 2024-07-23 2024-08-20 四川福莱宝生物科技有限公司 一种适用于高通量分析的生物元件数据分析方法

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WO2015039425A1 (fr) * 2013-09-18 2015-03-26 深圳迈瑞生物医疗电子股份有限公司 Dispositif et procédé pour tester le sexe d'un échantillon, et procédé de détermination de standard de jugement
WO2015154288A1 (fr) * 2014-04-10 2015-10-15 深圳迈瑞生物医疗电子股份有限公司 Procédé de compensation automatique, dispositif et cytomètre de flux correspondant
US9970857B2 (en) 2014-04-10 2018-05-15 Shenzhen Mindray Bio-Medical Electronics Co., Ltd. Automatic compensation method, device, and corresponding flow cytometer
WO2021154579A1 (fr) * 2020-01-31 2021-08-05 Becton, Dickinson And Company Procédés et systèmes pour ajuster une grille d'apprentissage pour recevoir des données de cytomètre en flux
CN116724222A (zh) * 2020-11-19 2023-09-08 贝克顿·迪金森公司 用于机器学习分析的细胞术数据的最佳缩放方法及其系统
US20220155209A1 (en) * 2020-11-19 2022-05-19 Becton, Dickinson And Company Method for Optimal Scaling of Cytometry Data for Machine Learning Analysis and Systems for Same
JP2023550130A (ja) * 2020-11-19 2023-11-30 ベクトン・ディキンソン・アンド・カンパニー 機械学習分析のためのサイトメトリックデータの最適なスケーリング方法及びそのシステム
EP4247255A4 (fr) * 2020-11-19 2024-05-22 Becton, Dickinson and Company Procédé pour la mise à l'échelle optimale de données de cytométrie pour une analyse par apprentissage machine, et systèmes associés
US12306087B2 (en) * 2020-11-19 2025-05-20 Becton, Dickinson And Company Method for optimal scaling of cytometry data for machine learning analysis and systems for same
JP7766690B2 (ja) 2020-11-19 2025-11-10 ベクトン・ディキンソン・アンド・カンパニー 機械学習分析のためのサイトメトリックデータの最適なスケーリング方法及びそのシステム
CN116642819A (zh) * 2023-07-19 2023-08-25 江苏得康生物科技有限公司 细胞群的识别方法及其装置
CN116642819B (zh) * 2023-07-19 2023-10-10 江苏得康生物科技有限公司 细胞群的识别方法及其装置
CN118226841A (zh) * 2024-05-23 2024-06-21 北京七星华创微电子有限责任公司 一种现场可编程门阵列fpga的测试方法
CN118520317A (zh) * 2024-07-23 2024-08-20 四川福莱宝生物科技有限公司 一种适用于高通量分析的生物元件数据分析方法

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