INTERCOLONIAL VARIABILITY IN MACROMOLECULAR COMPOSITION IN P‐STARVED AND P‐REPLETE SCENEDESMUS POPULATIONS REVEALED BY INFRARED MICROSPECTROSCOPY1
Abstract
Macromolecular variability in microalgal populations subject to different nutrient environments was investigated, using the chlorophyte alga Scenedesmus quadricauda (Turpin) Bréb. as a model organism. The large size of the four-cell coenobia in the strain used in this study (∼35 μm diameter) conveniently allowed high quality spectra to be obtained from individual coenobia using a laboratory-based Fourier transform infrared (FTIR) microscope with a conventional globar source of IR. By drawing sizable subpopulations of coenobia from two Scenedesmus cultures grown under either nutrient-replete or P-starved conditions, the population variability in macromolecular composition, and the effects of nutrient change upon this, could be estimated. On average, P-starved coenobia had higher carbohydrate and lower protein absorbance compared with P-replete coenobia. These parameters varied between coenobia with histograms of the ratio of absorbance of the largest protein and carbohydrate bands being Gaussian distributed. Distributions for the P-replete and P-starved subpopulations were nonoverlapping, with the difference in mean ratios for the two populations being statistically significant. Greater variance was observed in the P-starved subpopulation. In addition, multivariate models were developed using the spectral data, which could accurately predict the nutrient status of an independent individual coenobium, based on its FTIR spectrum. Partial least squares discriminant analysis (PLS-DA) was a better prediction method compared with soft independent modeling by class analogy (SIMCA).
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
FTIR with multivariate analysis has been used successfully to classify species and to differentiate the nutritional status of microalgae.
“The application of FTIR to classify species with the help of multivariate analysis [24 -29] and to differentiate the nutritional status of microalgae [30 -34] have been attempted successfully.”
From Response to nutrient variation on lipid productivity in green microalgae captured using second derivative FTIR and Raman spectroscopy (2022) · cites this paper as [32]
Cites
Stochastic Gene Expression in a Single Cell
Elowitz, M. B., Levine, A. J., Siggia, E. D., & Swain, P. S. (2002) · Science · doi:10.1126/science.1070919 · Cited by 5,441 · see citing works
Pattern recognition by means of disjoint principal components models
Wold, S. (1976) · Pattern Recognition · doi:10.1016/0031-3203(76)90014-5 · Cited by 1,166 · see citing works
Phinney, D. G., Kopen, G. C., Isaacson, R. L., & Prockop, D. J. (1999) · Journal of Cellular Biochemistry · doi:10.1002/(sici)1097-4644(19990315)72:4 · Cited by 545 · see citing works
Notes on the history and nature of partial least squares (PLS) modelling
Geladi, P. L. M. (1988) · Journal of Chemometrics · doi:10.1002/cem.1180020403 · Cited by 310 · see citing works
Giordano, M., Kansiz, M., Heraud, P., Beardall, J., Wood, B., & McNaughton, D. (2001) · Journal of Phycology · doi:10.1046/j.1529-8817.2001.037002271.x · Cited by 301 · see citing works
Self-citationGene expression and the myth of the average cell
Levsky, J. M. (2002) · Trends in Cell Biology · doi:10.1016/s0962-8924(02)00002-8 · Cited by 250 · see citing works
Heraud, P., Wood, B. R., Beardall, J., & McNaughton, D. (2006) · Journal of Chemometrics · doi:10.1002/cem.990 · Cited by 143 · see citing works
Self-citationHeraud, P., Wood, B. R., Tobin, M. J., Beardall, J., & McNaughton, D. (2005) · FEMS Microbiology Letters · doi:10.1016/j.femsle.2005.06.021 · Cited by 123 · see citing works
Self-citationFiorentino, F. (2003) · Molecular Human Reproduction · doi:10.1093/molehr/gag046 · Cited by 102 · see citing works
A comparison of methods for detection of phosphate limitation in microalgae
Beardall, J., Berman, T., Heraud, P., Kadiri, M. O., Light, B., Patterson, G., Roberts, S., Sulzberger, B., Sahan, E., Uehlinger, U., et al. (2001) · Aquatic Sciences · doi:10.1007/pl00001342 · Cited by 100 · see citing works
Self-citationInfrared imaging: Synchrotrons vs. arrays, resolution vs. speed
Levenson, E., Lerch, P., & Martin, M. C. (2006) · Infrared Physics & Technology · doi:10.1016/j.infrared.2006.01.026 · Cited by 69 · see citing works
Liang, Y., Beardall, J., & Heraud, P. (2006) · Botanica Marina · doi:10.1515/bot.2006.021 · Cited by 56 · see citing works
Self-citationAkhlaghi, Y., & Kompany‐Zareh, M. (2006) · Journal of Chemometrics · doi:10.1002/cem.971 · Cited by 55 · see citing works
Clonal Variation for Tolerance to Polyethylene Glycol-Induced Water Stress in Cultured Tomato Cells
Handa, A. K., Bressan, R. A., Handa, S., et al. (1983) · PLANT PHYSIOLOGY · doi:10.1104/pp.72.3.645 · Cited by 37 · see citing works
Probing the Influence of the Environment on Microalgae Using Infrared and Raman Spectroscopy
Heraud, P., Wood, B. R., Beardall, J., & McNaughton, D. (2007) · ACS symposium series · doi:10.1021/bk-2007-0963.ch007 · Cited by 27 · see citing works
Self-citationStudies on Scenedesmus costato-granulatus Skuja
Hegewald, E., Krienitz, L., & Schnepf, E. (1994) · Nova Hedwigia · doi:10.1127/nova.hedwigia/59/1995/97 · Cited by 21 · see citing works
A liability theory of disease: the foundation of cell population pathology
Zhao, J. (1997) · Medical Hypotheses · doi:10.1016/s0306-9877(97)90104-3 · Cited by 7 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
FT-IR Microspectroscopy Enhances Biological and Ecological Analysis of Algae
Murdock, J. N., & Wetzel, D. L. (2009) · Applied Spectroscopy Reviews · doi:10.1080/05704920902907440 · Cited by 223 · see citing works
Algal Biomass Analysis by Laser-Based Analytical Techniques—A Review
Pořízka, P., Prochazková, P., Prochazka, D., Sládková, L., Novotný, J., Petrilak, M., Brada, M., Samek, O., Pilát, Z., Zemánek, P., et al. (2014) · Sensors · doi:10.3390/s140917725 · Cited by 61 · see citing works
Podevin, M., Fotidis, I. A., & Angelidaki, I. (2018) · Critical Reviews in Biotechnology · doi:10.1080/07388551.2017.1398132 · Cited by 34 · see citing works
Esther Elizabeth Grace, C., Briget Mary, M., Vaidyanathan, S., & Srisudha, S. (2022) · Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy · doi:10.1016/j.saa.2021.120830 · Cited by 21 · see citing works
- Authors
- Heraud, P., Stojkovic, S., Beardall, J., McNaughton, D., & Wood, B. R.
- Affiliations
- Monash University (AU)
- Corresponding author
- Heraud, Philip
- Venue
- Journal of Phycology
- Publisher
- Wiley
- Published
- 2008-10
- Volume
- 44
- Issue
- 5
- Pages
- 1335-1339
- ISSN
- 0022-3646 (ISSN-L)
- 1529-8817
- OpenAlex
- W1986105905
- Cited by
- 32
- References
- 17
- Citations by year
- 2024: 3 · 2023: 1 · 2022: 1 · 2021: 1 · 2020: 3 · 2019: 1 · 2017: 3 · 2016: 1 · 2015: 2 · 2014: 5 · 2013: 3 · 2012: 3
- FWCI
- 0.90
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Phycology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Spectroscopy Techniques in Biomedical and Chemical Research
- Physical Sciences › Chemistry › Analytical Chemistry › Spectroscopy and Chemometric Analyses
- Physical Sciences › Environmental Science › Environmental Chemistry › Aquatic Ecosystems and Phytoplankton Dynamics
- Keywords
- Scenedesmus quadricauda, phosphorus deficiency, macromolecular composition, intraspecific variation, coenobia, carbohydrate content, protein content, FTIR microspectroscopy, PLS-DA, SIMCA
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review