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Difference between revisions of "Gnaiger 2021 MitoFit BCA"

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== Acknowledgements ==
== Acknowledgements ==
:::: I thank [[Zdrazilova Lucie |Lucie Zdrazilova]] for collaboration and input from our parallel manuscript, and [[Zischka Hans |Hans Zischka]], [[Garcia-Roves Pablo Miguel |Pablo M Garcia-Roves]], [[Torres-Quesada Omar |Omar Torres-Quesada]], [[Molina Anthony JA |Anthony JA Molina]], [[Kramer Philip A |Philip A Kramer]], [[Gonzalez-Armenta Jenny L |Jenny L Gonzalez-Armenta]], [[Grings Mateus |Mateus Grings]], and the Oroboros team ― particularly [[Donnelly Chris |Chris Donnelly]], [[Komlodi Timea |Timea Komlódi]], [[Schmitt Sabine |Sabine Schmitt]], and [[Doerrier Carolina |Carolina Doerrier]] ― for stimulating discussions and critical comments.  
:::: I thank [[Zdrazilova Lucie |Lucie Zdrazilova]] for collaboration and input from our parallel manuscript, and [[Zischka Hans |Hans Zischka]], [[Garcia-Roves Pablo Miguel |Pablo M Garcia-Roves]], [[Torres-Quesada Omar |Omar Torres-Quesada]], [[Molina Anthony JA |Anthony JA Molina]], [[Kramer Philip A |Philip A Kramer]], [[Gonzalez-Armenta Jenny L |Jenny L Gonzalez-Armenta]], [[Grings Mateus |Mateus Grings]], and the Oroboros team ― particularly [[Donnelly Chris |Chris Donnelly]], [[Schmitt Sabine |Sabine Schmitt]], [[Komlodi Timea |Timea Komlódi]], and [[Doerrier Carolina |Carolina Doerrier]] ― for stimulating discussions and critical comments.
 
::::* [[Zdrazilova Lucie |Lucie Zdrazilova]]: see Zdrazilova L, Hansikova H, Gnaiger E (2021) Comparable respiratory activity in attached and suspended human fibroblasts. MitoFit Preprints 2021.7. doi:10.26124/mitofit:2021-0007
 
::::* [[Donnelly Chris |Chris Donnelly]](0.1 2021-06-15): One important point I believe is worthy to mention here is that in the YĂ©pez paper they did not titrate Ama. I believe ''Rox'' is after Rot titration.
 
::::* [[Schmitt Sabine |Sabine Schmitt]] (v0.1 2021-06-15): The term concept-driven normalization requires explanation.
 
::::* [[Komlodi Timea |Timea Komlódi]] (v0.1 2021-06-15): I found it very useful how you explain the different outlier levels and use these for excluding data. Regarding negative values, sometimes we observe negative O<sub>2</sub> flux in the ROX state which is usually used for baseline correction. Can we use this “negative ''Rox''” for correction which would lead to higher O<sub>2</sub> fluxes? Or should we exclude those files where ''Rox'' is negative? Or in this case instead of baseline-corrected O<sub>2</sub> flux we can only use flux control ratios in the whole study?
 
::::* [[Zischka Hans |Hans Zischka]] (v0.2 2021-06-16): I would separate the discussion and conclusions. The conclusions are at best unclear.
 
::::* [[Garcia-Roves Pablo Miguel |Pablo M Garcia-Roves]] (v0.2 2021-06-16): I like the topic of the manuscript but I think it needs additional work to get the message deliver clearly. In my opinion it needs a different structure where you clearly define sequential steps in your rational and analysis. For example, Figure 1 address several issues at the same time and it could be confusing at the time to assess data representation. One part of the manuscript is dedicated to explain how data analysis has been performed (very important and informative: PSI, ''OSI'', log-transformed; BCA, 
). But explanations about the procedure to perform data analysis are intercalated with data representation and analysis. The work brings to my attention a way to analyze data sets that could be of interest, as you mentioned in the manuscript, for data collected during the MitoEAGLE COST action.
 
::::* [[Molina Anthony JA |Anthony JA Molina]] (version 0.4 2021-06-24): Take a look at this paper that just came out ([[Zhang 2021 PLOS ONE |attached]]) about the same dataset.
 
::::* [[Doerrier Carolina |Carolina Doerrier]] (version 0.4 2021-06-24): The most difficult part to follow was the ''OSI'' and the most stimulating sections those showing the bioenergetic clusters.
 
::::* [[Torres-Quesada Omar |Omar Torres-Quesada]] (version 0.4 2021-06-24): Concerning the threshold values for detection of outliers (Figure 7, page 13), are these values specifically calculated for each data set or are they standard? This would be quite interesting to know if someone wants to use this analysis which own data. The BCA outlier level workflow shows a stepwise strategy to remove outliers from large-scale data. Would it be possible to apply this strategy with small-scale data? This would be relevant for example for BCA of O2k data (usually with lower population size). The excel file I find it quite useful when somebody wants to apply the same workflow with own data.
 
::::* [[Grings Mateus |Mateus Grings]] (v0.9, 2021-08-30): I really liked the ideas and data of the manuscript and agree that bioenergetic cluster analysis is a very interesting approach to analyze and compare bioenergetics data from different cells and performed with different instruments. Besides that, it was importantly pointed out that it is a great tool that can help in the evaluation of reproducibility of data. It is interesting that since it is a more visual approach, it is easier to observe differences among different data and also for the efficient detection of outliers. It was very easy and transparent to visualize the outliers and understand the problems related to them in the graphical analyzes performed for their characterization (Figures 9 and 10). I liked the general idea of normalization using internal experimental values for the comparison of data, excluding errors that may be induced by the addition of external values, such as differences in procedures to measure the external parameters in distinct laboratories. In addition, the use of normalization with flux control ratios and flux control efficiencies may show different aspects of the data. Although I do not have experience with cluster analysis and profound knowledge of all the mathematical concepts used to develop this methodology, the conceptual background was very clear and helpful for the understanding of the manuscript results. I did not profoundly understand all details in some of the results, but I could understand the main results and ideas of all of them, especially because of my previous knowledge on respirometry. I also found particularly interesting the models used in Figure 12 to observe the differentiation between the dyscapacity of fibroblasts from aged versus young donors and dyscoupling in senescent fibroblasts versus young proliferating and growth-arrested. Some problems pointed out about experiments performed with the Seahorse XF96 instrument were very relevant. Something that was mentioned and that I always think about is the use of a single uncoupler injection and the lack of uncoupler titration. Even though ideal concentrations of uncoupler for measuring ET capacity are tested in a pilot experiment for different cell types, the conditions of the cells or the assay may change in different experiments and situations, so that the uncoupler concentration needed for efficiently measuring ET capacity may vary (making it important to do a titration for each experiment). Another matter that I would like to comment about regards the normalization by cell number at the Seahorse XF96. I thought about it when I was reading the methodology used for NHDF in the Seahorse (normalization by seed count versus final cell count). When I performed some of my experiments in the Seahorse using fibroblasts from different patients with the same disease, I could not normalize the data using the seed count. This happened because the cells had very different proliferation rates and showed different adhesion patterns after seeding due to differences in patient genotypes and phenotypes. Therefore, I had to evaluate the cells after the assay to do the normalization. I think it is important to take this into account when performing normalization of experiments with attached cells.
 


== Support ==
== Support ==

Revision as of 21:37, 8 September 2021


MitoFit Preprints         MitoFit Preprints        
Gnaiger 2019 MitoFit Preprints
       
Gnaiger MitoFit Preprints 2020.4
        MitoFit DOI Data Center         MitoPedia: Preprints         Bioenergetics Communications


Gnaiger 2021 MitoFit BCA

Publications in the MiPMap
Gnaiger E (2021) Bioenergetic cluster analysis – mitochondrial respiratory control in human fibroblasts. MitoFit Preprints 2021.8. doi:10.26124/mitofit:2021-0008 (in preparation)

»

MitoFit pdf

Bioenergetic cluster analysis – mitochondrial respiratory control in human fibroblasts

Gnaiger Erich (2021-##-##) MitoFit Preprints

Abstract: Cell respiration reflects mitochondrial fitness and plays a pivotal role in health and disease. Despite the rapidly increasing number of applications of cell respirometry to address current challenges in biomedical research, cross-references are rare between respirometric projects and platforms. Evaluation of accuracy and reproducibility between laboratories requires presentation of results in a common format independent of the applied method. When cell respiration is expressed as oxygen consumption rate in an experimental chamber, normalization is mandatory for comparability of results. Concept-driven normalization and regression analysis are key towards bioenergetic cluster analysis presented as a graphical tool to identify discrete data populations.

In a meta-analysis of human skin fibroblasts, high-resolution respirometry and polarography covering cell senescence and the human age range are compared with multiwell respirometry. The common coupling control protocol measures ROUTINE respiration of living cells followed by sequential titrations of oligomycin, uncoupler, and inhibitors of electron transfer.

Bioenergetic cluster analysis increases the resolution of outliers within and differences between groups. An outlier-skewness index is introduced as a guide towards logarithmic transformation for statistical analysis. Isolinear clusters are separated by variations in the extent of a quantity that correlates with the rate, whereas heterolinear clusters fall on different regression lines. Dispersed clusters are clouds of data separated by a critical threshold value. Bioenergetic cluster analysis provides new insights into mitochondrial respiratory control and a guideline for establishing a quality control paradigm for bioenergetics and databases in mitochondrial physiology. ‱ Keywords: human dermal fibroblasts HDF, living cells ce, cell respiration, coupling control, oxidative phosphorylation OXPHOS, age, senescence, bioenergetic cluster analysis BCA, meta-analysis, normalization, high-resolution respirometry HRR, Oroboros O2k, Seahorse XF Analyzer, outlier-skewness index OSI, regression analysis ‱ Bioblast editor: Gnaiger E ‱ O2k-Network Lab: AT Innsbruck Oroboros

ORCID: ORCID.png Gnaiger Erich

References

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2020
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2021
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2020
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Cited by

  • Zdrazilova L, Hansikova H, Gnaiger E (2021) Comparable respiratory activity in attached and suspended human fibroblasts. MitoFit Preprints 2021.7. doi:10.26124/mitofit:2021-0007 - »Bioblast link«
  • KomlĂłdi T, Cardoso LHD, Doerrier C, Moore AL, Rich PR, Gnaiger E (2021) Coupling and pathway control of coenzyme Q redox state and respiration in isolated mitochondria. Bioenerg Commun 2021.3. https://doi.org/10.26124/bec:2021-0003


Keywords


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Acknowledgements

I thank Lucie Zdrazilova for collaboration and input from our parallel manuscript, and Hans Zischka, Pablo M Garcia-Roves, Omar Torres-Quesada, Anthony JA Molina, Philip A Kramer, Jenny L Gonzalez-Armenta, Mateus Grings, and the Oroboros team ― particularly Chris Donnelly, Sabine Schmitt, Timea Komlódi, and Carolina Doerrier ― for stimulating discussions and critical comments.
  • Lucie Zdrazilova: see Zdrazilova L, Hansikova H, Gnaiger E (2021) Comparable respiratory activity in attached and suspended human fibroblasts. MitoFit Preprints 2021.7. doi:10.26124/mitofit:2021-0007
  • Chris Donnelly(0.1 2021-06-15): One important point I believe is worthy to mention here is that in the YĂ©pez paper they did not titrate Ama. I believe Rox is after Rot titration.
  • Sabine Schmitt (v0.1 2021-06-15): The term concept-driven normalization requires explanation.
  • Timea KomlĂłdi (v0.1 2021-06-15): I found it very useful how you explain the different outlier levels and use these for excluding data. Regarding negative values, sometimes we observe negative O2 flux in the ROX state which is usually used for baseline correction. Can we use this “negative Rox” for correction which would lead to higher O2 fluxes? Or should we exclude those files where Rox is negative? Or in this case instead of baseline-corrected O2 flux we can only use flux control ratios in the whole study?
  • Hans Zischka (v0.2 2021-06-16): I would separate the discussion and conclusions. The conclusions are at best unclear.
  • Pablo M Garcia-Roves (v0.2 2021-06-16): I like the topic of the manuscript but I think it needs additional work to get the message deliver clearly. In my opinion it needs a different structure where you clearly define sequential steps in your rational and analysis. For example, Figure 1 address several issues at the same time and it could be confusing at the time to assess data representation. One part of the manuscript is dedicated to explain how data analysis has been performed (very important and informative: PSI, OSI, log-transformed; BCA, 
). But explanations about the procedure to perform data analysis are intercalated with data representation and analysis. The work brings to my attention a way to analyze data sets that could be of interest, as you mentioned in the manuscript, for data collected during the MitoEAGLE COST action.
  • Carolina Doerrier (version 0.4 2021-06-24): The most difficult part to follow was the OSI and the most stimulating sections those showing the bioenergetic clusters.
  • Omar Torres-Quesada (version 0.4 2021-06-24): Concerning the threshold values for detection of outliers (Figure 7, page 13), are these values specifically calculated for each data set or are they standard? This would be quite interesting to know if someone wants to use this analysis which own data. The BCA outlier level workflow shows a stepwise strategy to remove outliers from large-scale data. Would it be possible to apply this strategy with small-scale data? This would be relevant for example for BCA of O2k data (usually with lower population size). The excel file I find it quite useful when somebody wants to apply the same workflow with own data.
  • Mateus Grings (v0.9, 2021-08-30): I really liked the ideas and data of the manuscript and agree that bioenergetic cluster analysis is a very interesting approach to analyze and compare bioenergetics data from different cells and performed with different instruments. Besides that, it was importantly pointed out that it is a great tool that can help in the evaluation of reproducibility of data. It is interesting that since it is a more visual approach, it is easier to observe differences among different data and also for the efficient detection of outliers. It was very easy and transparent to visualize the outliers and understand the problems related to them in the graphical analyzes performed for their characterization (Figures 9 and 10). I liked the general idea of normalization using internal experimental values for the comparison of data, excluding errors that may be induced by the addition of external values, such as differences in procedures to measure the external parameters in distinct laboratories. In addition, the use of normalization with flux control ratios and flux control efficiencies may show different aspects of the data. Although I do not have experience with cluster analysis and profound knowledge of all the mathematical concepts used to develop this methodology, the conceptual background was very clear and helpful for the understanding of the manuscript results. I did not profoundly understand all details in some of the results, but I could understand the main results and ideas of all of them, especially because of my previous knowledge on respirometry. I also found particularly interesting the models used in Figure 12 to observe the differentiation between the dyscapacity of fibroblasts from aged versus young donors and dyscoupling in senescent fibroblasts versus young proliferating and growth-arrested. Some problems pointed out about experiments performed with the Seahorse XF96 instrument were very relevant. Something that was mentioned and that I always think about is the use of a single uncoupler injection and the lack of uncoupler titration. Even though ideal concentrations of uncoupler for measuring ET capacity are tested in a pilot experiment for different cell types, the conditions of the cells or the assay may change in different experiments and situations, so that the uncoupler concentration needed for efficiently measuring ET capacity may vary (making it important to do a titration for each experiment). Another matter that I would like to comment about regards the normalization by cell number at the Seahorse XF96. I thought about it when I was reading the methodology used for NHDF in the Seahorse (normalization by seed count versus final cell count). When I performed some of my experiments in the Seahorse using fibroblasts from different patients with the same disease, I could not normalize the data using the seed count. This happened because the cells had very different proliferation rates and showed different adhesion patterns after seeding due to differences in patient genotypes and phenotypes. Therefore, I had to evaluate the cells after the assay to do the normalization. I think it is important to take this into account when performing normalization of experiments with attached cells.


Support

Template NextGen-O2k.jpg
This work was partially funded by the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 859770, NextGen-O2k project. Contribution to the MitoEAGLE Task Group of the Mitochondrial Physiology Society.


Labels: MiParea: Respiration, Instruments;methods  Pathology: Aging;senescence 

Organism: Human  Tissue;cell: Fibroblast  Preparation: Intact cells 

Regulation: Coupling efficiency;uncoupling  Coupling state: LEAK, OXPHOS, ET  Pathway: ROX  HRR: Oxygraph-2k 

SUIT-003, MitoEAGLEPublication, MitoFit 2021 ace-sce, MitoFit 2021 CoQ 

Term Link to MitoPedia term Symbol Unit Links and comments
catabolic rate of respiration Cell respiration JkO2; IkO2 varies flux J versus flow I
catabolic reaction Cell respiration k -
cell count Count Nce [x] see number of cells; countable object s=ce
cell-count concentration Concentration Cce [x∙L­-1] Cce = Nce∙V-1; count concentration C versus amount concentration c; subscript ce indicates the entity type: concentration of ce. But it does not signal 'per entity', which would be written as 'per cell' Xce.
cell mass Body mass mce [kg] mass of cells m versus mass per cell (per single entity cell) MXce
cell mass, mass per cell Body mass MXce [kg∙x­-1] mass per single cell MXce; upper case M and subscript X signal 'per count', subscript ce signals the entity s=ce; in a context restricted to cells or molecules or a particular organism such as humans, the abbreviated symbol M [kg∙x­-1] provides a sufficiently informative signal, particularly in combination with the explicit unit.
cell-mass concentration in chamber Concentration Cmce [kg∙L­-1] see Cms: Cmce = mce∙V-1; upper case C alone would signal 'count concentration' (CN is more explicit), whereas the signal for 'mass concentration' is in the combination Cm.
concentration of O2, amount Concentration cO2 = nO2∙V­-1 [mol∙L­-1] [O2]
concentration of s, count Concentration Cs = Ns∙V­-1 [x∙L-1] (number concentration Cohen 2008 IUPAC Green Book); the signal for count concentration is given by the upper case C in contrast to c for amount concentration. In both cases, the subscript X indicates the entity type, not to be confused with a number of entities.
count of Xs Count Ns [x] SI; see number of entities Xs
coupling control Coupling-control ratio CCR -
coupling control state Coupling control state CCS -
electron transfer pathway Electron transfer pathway ET pathway -
electron transfer, state Electron transfer pathway ET - (State 3u)
electron transfer system Electron transfer pathway ETS - (electron transport chain)
elementary entity Entity Xs [x] single countable object of sample type s
ET capacity ET capacity E varies rate
flow, for O2 Flow IO2 [mol∙s-­1] system-related extensive quantity
flux, for O2 Flux JO2 varies size-specific quantity
flux control ratio Flux control ratio FCR 1 background/reference flux
International System of Units International System of Units SI - Cohen 2008 IUPAC Green Book
LEAK state LEAK respiration LEAK - (compare State 4)
LEAK respiration LEAK respiration L varies rate
living cells Living cells ce - (intact cells)
mass concentration of sample s in chamber Concentration Cms [kg∙L-1]
mass of sample s in a mixture Mass ms [kg] SI: mass of pure sample mS
mass per single object Body mass MNX [kg∙x­1] SI: m(X); compare molar mass M(X)
mitochondria or mitochondrial Mitochondria mt -
mitochondrial concentration Mitochondrial marker, Concentration CmtE = mtE∙V-1 [mtEU∙L-1]
mitochondrial content per X Mitochondrial marker mtENX [mtEU∙x­-1] mtENX = mtE∙NX-1
mitochondrial elementary marker Mitochondria mtE [mtEU] quantity of mt-marker
mitochondrial elementary unit Mitochondria mtEU varies specific units for mt-marker
MitoPedia MitoPedia, MitoPedia: Respiratory states
normalization of rate Normalization of rate - -
number of cells Count Nce [x] total cell count of living cells, Nce = Nvce + Ndce
oxidative phosphorylation Oxidative phosphorylation OXPHOS -
OXPHOS-capacity OXPHOS-capacity P varies rate
OXPHOS state OXPHOS-capacity OXPHOS - OXPHOS-state distinguished from the process OXPHOS (State 3 at kinetically-saturating [ADP] and [Pi])
oxygen concentration Oxygen concentration cO2 = nO2∙V­-1 [mol∙L­-1] [O2]
oxygen solubility Oxygen solubility SO2 [”mol·kPa-1]
oxygen flux, in reaction r Oxygen flux JrO2 varies
quantities, symbols, and units Quantities, symbols, and units - - An explanation of symbols and unit [x]
rate in ET state Electron transfer pathway E varies ET capacity
rate in LEAK state LEAK respiration L varies L(Omy)
rate in ROX state Residual oxygen consumption Rox varies
residual oxygen consumption Residual oxygen consumption ROX; Rox - state ROX; rate Rox
respiration Respirometry JrO2 varies rate of reaction r
respiratory state MitoPedia: Respiratory states - -
steay state Steady state - -
substrate-uncoupler-inhibitor-titration Substrate-uncoupler-inhibitor titration SUIT -
system System - -
unit elementary entity Entity UX [x] single countable object
uncoupling Uncoupler titrations - -
volume of experimental chamber Volume V [L] liquid volume V including the sample s