Darya Orlova
28 June, 2016
- Главная
- Разное
- Дизайн
- Бизнес и предпринимательство
- Аналитика
- Образование
- Развлечения
- Красота и здоровье
- Финансы
- Государство
- Путешествия
- Спорт
- Недвижимость
- Армия
- Графика
- Культурология
- Еда и кулинария
- Лингвистика
- Английский язык
- Астрономия
- Алгебра
- Биология
- География
- Детские презентации
- Информатика
- История
- Литература
- Маркетинг
- Математика
- Медицина
- Менеджмент
- Музыка
- МХК
- Немецкий язык
- ОБЖ
- Обществознание
- Окружающий мир
- Педагогика
- Русский язык
- Технология
- Физика
- Философия
- Химия
- Шаблоны, картинки для презентаций
- Экология
- Экономика
- Юриспруденция
Quantitative flow cytometry. Advancing the ability of flow cytometry to serve clinical and research purposes презентация
Содержание
- 1. Quantitative flow cytometry. Advancing the ability of flow cytometry to serve clinical and research purposes
- 2. My story : Biysk
- 3. My story : Novosibirsk Biysk
- 4. My story : Novosibirsk B.S. and M.S. in Physics
- 5. My story : Novosibirsk Ph.D. in
- 6. My story : Novosibirsk Ph.D. in
- 7. My story : Novosibirsk Brno Stanford, CA
- 8. What is the problem? Flow cytometry is
- 9. To develop methods and procedures to enable
- 10. Accurate classification and enumeration of cells with
- 11. Classification and enumeration of cells with
- 12. Schematic of the analysis package that statistical procedures are embedded http://cytogenie.org/
- 13. Projection pursuit seeks one projection at a time http://www.few.vu.nl/~tvpham/images/ppde.jpg http://slideplayer.com/slide/4970323/# Projection Pursuit
- 14. why: Curse of dimensionality Less
- 15. User-Guided Projection Pursuit
- 16. Walther G. et al, Adv Bioinformatics, 2009 Finding clusters by density based merging (DBM)
- 18. How different are these two samples?
- 19. The index should: possess the properties
- 20. Some test statistics are limited to univariate
- 21. Probability binning plateaus but EMD increases monotonically
- 22. EMD is the minimum cost of turning
- 23. Assume two distributions represented by signatures, P
- 24. Diagnostic tool for distinguishing cystic fibrosis
- 25. Multiparameter diagnostic tool in CF and
- 26. EMD scores based on expression of two
- 27. Cluster matching
- 28. Quantitation of expression levels of surface and intracellular protein biomarkers
- 30. Experiment: kinetic of
- 31. total number of receptor
- 32. Obtained distributions of neutrophils on the number
- 33. As a solution to this problem
- 34. Chothia et al., Nature, 1989
- 35. Approximation of antigen binding site shape using rectangular “binding spot” model
- 36. From the binding rate constant k+,
- 37. Empirical binding rate constant values and
- 38. The rate constants estimated using binding site
- 39. Physics of chemoreception. HC Berg & EM Purcell, Biophys J 20, 93-219 (1977). Electrical analogue
- 40. Antigen “effective binding site” radius as an
- 41. Functional epitope
- 42. Comparison of estimates for binding site radius
- 43. The rate constants estimated using binding
- 44. Practical application in quantitative agglutination test http://image.slidesharecdn.com/nephlerometryandturbidimetry-150203152442-conversion-gate02/95/nephlerometry-and-turbidimetry-6-638.jpg?cb=1422977133
- 45. Thank you! Darya Orlova, Ph.D. Stanford University
- 46. [2] Moskalensky et al., 2015 [3] Xavier
- 47. Number of possible 2-D combinations in n dimensional space:
- 51. Silhouette coefficient
- 52. 1. Calculate silhouette coef. (SC) For
- 53. Summary of diffusion parameters for six well studied antigen-antibody complexes in water solution
- 54. Physics of chemoreception. HC Berg & EM Purcell, Biophys J 20, 93-219 (1977).
My story : Biysk
Слайд 1Quantitative Flow Cytometry: advancing the ability of flow cytometry
to serve
clinical and research purposes
Слайд 8What is the problem?
Flow cytometry is an essential tool for basic
immunological research, clinical discovery of potential therapeutics, development and approval of drugs and devices, disease diagnosis, and therapeutic treatment and monitoring.
However, the measurements made on different instrument platforms at different times and places often cannot be compared. Such discrepancies between and among measurements accompanied by discrepancies in data analysis procedures introduce uncertainty in diagnostic decisions, and impedes advances in basic science.
However, the measurements made on different instrument platforms at different times and places often cannot be compared. Such discrepancies between and among measurements accompanied by discrepancies in data analysis procedures introduce uncertainty in diagnostic decisions, and impedes advances in basic science.
http://www.nist.gov/mml/bbd/bioassay/quantitative_flow_cytometry.cfm
Слайд 9To develop methods and procedures to enable quantitative measurements of biological
substances such as cells, proteins, etc.
By providing quantitative flow cytometry measurement solutions, we ensure that researchers can produce better data, better drugs are developed, and patients get better treatment in a clinical setting.
By providing quantitative flow cytometry measurement solutions, we ensure that researchers can produce better data, better drugs are developed, and patients get better treatment in a clinical setting.
The objective :
Слайд 10Accurate classification and enumeration of cells with specific phenotypic characteristics.
Quantitation of
expression levels of surface and intracellular protein biomarkers.
Слайд 11Classification and enumeration of cells with
specific phenotypic characteristics
Marginal Zone
B cells
Follicular
B cells
Immature B + B-1 a cells
Слайд 12Schematic of the analysis package that statistical procedures are embedded
http://cytogenie.org/
Слайд 13Projection pursuit seeks one projection at a time
http://www.few.vu.nl/~tvpham/images/ppde.jpg
http://slideplayer.com/slide/4970323/#
Projection Pursuit
Слайд 14
why:
Curse of dimensionality
Less Robustness
Required number of events increases with dimensionality
Greater
computational cost
...
...
http://www.newsnshit.com/curse-of-dimensionality-interactive-demo/
Projection Pursuit
Слайд 19The index should:
possess the properties of a metric:
non-negativity d(x,y) ≥
0
identity of indiscernibles d(x,y) = 0, if and only if x = y
symmetry d(x,y) = d(y,x)
triangle inequality d(x,z) ≤ d(x,y) + d(y,z)
be robust with respect to small changes
be non-parametric
be computationally efficient
identity of indiscernibles d(x,y) = 0, if and only if x = y
symmetry d(x,y) = d(y,x)
triangle inequality d(x,z) ≤ d(x,y) + d(y,z)
be robust with respect to small changes
be non-parametric
be computationally efficient
Goals:
quantitate differences between samples and provide a standard error
identify changes in joint expression of multiple markers
appropriately rank test samples based on the amount of deviation from controls
Quantification index allows biologically informative interpretation
Слайд 20Some test statistics are limited to univariate data, e.g., Kolmogorov-Smirnoff statistic
and Overton Subtraction (Sheskin, 2000)
Recent methods can be applied to multivariate flow cytometry
Probability Binning (Roederer et al., 2001)
Frequency Difference Gating (Roederer and Hardy, 2001)
Cytometric Fingerprinting (Rogers et al., 2008)
Quadratic Form Metric (Bernas et al., 2008)
Recent methods can be applied to multivariate flow cytometry
Probability Binning (Roederer et al., 2001)
Frequency Difference Gating (Roederer and Hardy, 2001)
Cytometric Fingerprinting (Rogers et al., 2008)
Quadratic Form Metric (Bernas et al., 2008)
How different are samples?
With respect to both the proportion of cells whose marker expression has changed and the magnitude of the change
Most of the current methods ask “Do samples differ?”
Слайд 21Probability binning plateaus but EMD increases monotonically as one population moves
further from the center of the other
Слайд 22EMD is the minimum cost of turning one pile of dirt
into the other where the cost is the amount of dirt moved times the distance by which it is moved
The biological interpretation of the EMD between two flow cytometry samples includes both the proportion of cells whose marker expression has changed
and the magnitude of the change
Earth mover's distance (EMD)
Слайд 23Assume two distributions represented by signatures, P = {(p1,wp1),…,(pm,wpm)} and Q={(q1,wq1),…,(qn,wqn)}
where
pi,qi are bin centroids with frequencies wpi,wqi,
D = [dij] is the matrix of Euclidean distances between pi and qj for all i,j.
Find a flow F = [fij] between pi and qj that minimizes the total cost:
The optimal flow F between the source and destination signatures is determined by solving the linear programming problem.
EMD is then defined as a function of the optimal flow F=[fij] and the ground distance D=[dij]
Data were binned into the groups used in the signature according to (Roederer et al., 2001)
Earth mover's distance (EMD)
in terms of a linear programming problem
Слайд 24 Diagnostic tool for distinguishing cystic fibrosis (CF) from allergic bronchopulmonary
aspergillosis (ABPA) in CF
Surface CD203c in blood basophils after ex vivo stimulation with A. fumigatus (Af) allergen/extract (offending) or peanut (non offending) allergen. Gernez et al. J Cyst Fibros 11:502-10, 2012
Blood basophils from patients with ABPA are
hyper-responsive to stimulation by Af allergen
Antibiotics
Corticosteroids
Anti-fungal
medicines
Слайд 25 Multiparameter diagnostic tool in CF and CF-ABPA
Compare basophil response to
stimulation with the A. fumigatus allergen/extract
Total white blood cells (FSС-A/SSC-A)→singlets (FSС-A/FSC-H)→CD41a--live (CD41a/live/dead)→
Dump--CD123++ (CD3, CD66b, HLA-DR/CD123)
Слайд 26EMD scores based on expression of two independent flow cytometry markers
more accurately distinguish allergic (CF-ABPA) from non-allergic (CF) patients
Слайд 29
cell
cell
cell
We previously suggested an antigen concentration quantification approach which utilizes the
value of the binding rate constant for each particular monoclonal antibody-antigen reaction measured with flow cytometry
Antigen-antibody interactions on the surface of cells
Слайд 30
Experiment: kinetic of mean fluorescence
0.16 min
1 min
3 min
9 min
27 min
Beads or
blood
sample (fix)
sample (fix)
Antibody*
Слайд 31
total number of receptor in the volume unit (free+occupied);
mean fluorescence value
of cell
Mathematical model: kinetic of mean fluorescence
Reaction rate constant
Initial antibody concentration
n – binding sites per one bead/cell
K+
A0
Слайд 32Obtained distributions of neutrophils on the number
(in logarithmic scale) of
FcgRIIIb receptors for different donors
The total number of cells is the same for each histogram
Слайд 33
As a solution to this problem we introduce a theoretical approach
allowing predicting the binding rate constant for changes in experimental conditions.
We verify our theoretical approach comparing the results to experimentally measured binding rate constants for classical examples of monoclonal antibody-antigen interactions under different temperature regimes.
We verify our theoretical approach comparing the results to experimentally measured binding rate constants for classical examples of monoclonal antibody-antigen interactions under different temperature regimes.
However, application of such rate constant approach is currently limited by the lack of measured binding rate constant values for most antigen-antibody pairs of interest and changes in experimental conditions (temperature, viscosity, fluorescent labels, etc.)
Слайд 36
From the binding rate constant k+, it was possible to estimate
the radius b of the binding site (a circular approximation of the shape of the site placed on a spherical reagent) using following expression
where η is the viscosity of the media, kB is the Boltzmann constant, T is the temperature; R1 and R2 are radii, N1 and N2 are valences of the first and second reactants, correspondingly.
The radius of antibody molecules can be estimated from the diffusion coefficient using Stokes–Einstein equation :
On the other hand, the diffusion coefficient of the molecule can be estimated using the known relationship between the diffusion coefficient (in cm2 s-1) and the molar mass (in Da), M, of a protein (in water at room temperature)
Слайд 37
Empirical binding rate constant values and corresponding calculated effective binding site
radii for 6 antigen-antibody complexes at room temperature
Слайд 38The rate constants estimated using binding site radius value for room
temperature are in good agreement with published empirical binding rate constants for different temperature regimes
Слайд 39Physics of chemoreception. HC Berg & EM Purcell, Biophys J 20,
93-219 (1977).
Electrical analogue
Слайд 40Antigen “effective binding site” radius as an equivalent of plate capacitor
capacitance
“Effective binding site” radius (b) can be calculated using the following expression:
where a and c are maximum length and maximum width (assuming a>c) of dominant amino acid residuals respectively (e.g. can be determine using HyperChem 7.5 software)
Слайд 42Comparison of estimates for binding site radius electrostatic analogues with effective
binding site radii calculated using empirical rate constant values
Слайд 43
The rate constants estimated using binding site radius value for room
temperature are in good agreement with published empirical binding rate constants for different temperature regimes
*
Слайд 44Practical application in quantitative agglutination test
http://image.slidesharecdn.com/nephlerometryandturbidimetry-150203152442-conversion-gate02/95/nephlerometry-and-turbidimetry-6-638.jpg?cb=1422977133
Слайд 45Thank you!
Darya Orlova, Ph.D.
Stanford University School of Medicine
Genetics Department
Beckman Building,
Room B013
279 Campus Drive, Stanford, CA 94305
orlova@stanford.edu
279 Campus Drive, Stanford, CA 94305
orlova@stanford.edu
Слайд 46[2] Moskalensky et al., 2015
[3] Xavier et al., 1998
[4] Xavier et
al., 1999
I5] Ito et al., 1995
[6] Raman et al., 1992
[7] Nekrasov VM et al., 2014
[8] Ibrahim, M., et al., 1998.
[9] Wibbenmeyer et al., 1999
[10] Sheriff et al., 1987
[11] Chacko et al., 1996
[12] Kam-Morgan et al., 1993
[13] Novotny, 1991
[14] Padlan et al., 1989
[15] Dall’Acqua et al., 1998
[16] Pierce et al., 1999
I5] Ito et al., 1995
[6] Raman et al., 1992
[7] Nekrasov VM et al., 2014
[8] Ibrahim, M., et al., 1998.
[9] Wibbenmeyer et al., 1999
[10] Sheriff et al., 1987
[11] Chacko et al., 1996
[12] Kam-Morgan et al., 1993
[13] Novotny, 1991
[14] Padlan et al., 1989
[15] Dall’Acqua et al., 1998
[16] Pierce et al., 1999
Слайд 52
1. Calculate silhouette coef. (SC) For each pair of clusters+their noise.
Calculate % frequency for each cluster. Find the most separated (based on SC) pair of clusters with highest % frequency (cluster a and b).
2. Assign all other clusters on this 2D plot either to cluster a or cluster b based on SC.
a
b
3. Now you have only two clusters A and B (for each possible 2D plot).
4. Calculate SC between A and B. Calculate % frequency for A and B. Now you can rank all possible 2D plots based on then SC and % frequency distribution between A and B. On the first place should be 2D plot with SC closest to 1 and % frequency distribution closest to 50/50 between A and B.
5. Pick A or B from most highly ranked 2D plot, project A(or B) to all possible 2D plots and proceed recursively with the same procedure (1-5).
Слайд 53Summary of diffusion parameters for six well studied antigen-antibody complexes
in
water solution
