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Diatoms

Diatoms are a diverse group of microscopic, single-celled algae that are found in a variety of aquatic environments.
These unicellular organisms have a unique cell wall composed of silica, which gives them a distinctive shell-like structure.
Diatoms play a crucial role in the global ecosystem, as they are a primary producer in aquatic food webs and contribute significantly to the world's oxygen supply.
Their fossilized remains also provide valuable insights into past climates and environmental conditions.
Diatom research is essential for understandinng the complex interactions between these organisms and their surrounding environment, as well as their potential applications in fields such as biofuel production, water quality monitoring, and paleoclimatology.
PubCompare.ai provides an innovative platform to streamline diatom research by empowering scientists to effortlessly locate and compare protocols from literature, pre-prints, and patents, leveraging AI-powered analysis to identify the most optimized and reproducible methods.

Most cited protocols related to «Diatoms»

1
Validating Cohesion Workflow with Phytoplankton Data
Cited 24 times
The North Temperate Lakes Long-Term Ecological Research database hosts many
long-term time ecological series. We used five long-term phytoplankton data sets
(two from the North Temperate Lakes Long-Term Ecological Research and three from
the Cascade research group) to validate the cohesion workflow. These data sets met
a number of criteria that made them good candidates for the validation: the
samples were collected regularly, sampling spanned multiple years and many
environmental gradients, and taxa were counted in absolute abundance. The term
‘phytoplankton’ refers to the polyphyletic assemblage of
photosynthetic aquatic microbes (Litchman and Klausmeier,
2008). The data sets are from the following lakes in Wisconsin, USA:
Lake Mendota (293 samples with 410 taxa over 19 years), Lake Monona (264 samples
with 382 taxa over 19 years), Paul Lake (197 samples with 209 taxa over 12 years),
Peter Lake (197 samples with 237 taxa over 12 years) and Tuesday Lake (115 samples
with 121 taxa over 12 years). These lakes vary in size, productivity and food web
structure. Lake Mendota and Lake Monona are large (39.4 km2 and
13.8 km2), urban, eutrophic lakes (Brock,
2012). Peter, Paul and Tuesday lakes are small (each
<0.03 km2) lakes surrounded by forest (Carpenter and Kitchell, 1996 ). Peter Lake and Tuesday Lake were also
subjected to whole-lake food web manipulations during the sampling timeframe
(detailed in Elser and Carpenter, 1988 (link) and Cottingham et al., 1998 ). After validating our
workflow using the phytoplankton data sets, we tested the cohesion metrics on a
bacterial data set obtained using 16S rRNA gene amplicon sequencing. These types
of data sets often contain thousands of taxa, most of them rare, which may
influence the results of correlation-based analyses (Faust
and Raes, 2012). We used the Lake Mendota bacterial 16S rRNA gene
sequencing time series (91 samples with 7081 taxa over 11 years) for this analysis
(Hall et al., in review ). Sample
processing, sequencing and core amplicon data analysis were performed by the Earth
Microbiome Project (www.earthmicrobiome.org; Gilbert et
al., 2014), and all amplicon sequence data and metadata have
been made public through the data portal (qiita.microbio.me/emp). Briefly, community DNA (Kara et al., 2013 (link)) was used to amplify partial 16S rRNA
genes using the 515F-806R primer pair (Caporaso et
al., 2011) and an Illumina MiSeq, with standard Earth
Microbiome Project protocols.
We present the workflow using results from the Lake Mendota phytoplankton data
set, as it is the largest (longest duration and most taxa) data set available in
absolute abundance. The dominant taxa in the Lake Mendota phytoplankton data set
change throughout the year, with diatoms most abundant during the spring bloom and
cyanobacteria most abundant in summer. Details about phytoplankton data sets can
be found at https://lter.limnology.wisc.edu/. Further details about the Lake
Mendota 16S rRNA gene data set are included in the Supplementary Online Material.
Herren C.M, & McMahon K.D. (2017). Cohesion: a method for quantifying the connectivity of microbial communities. The ISME Journal, 11(11), 2426-2438.
Publication 2017
Bacteria Diatoms Food Food Web Forests Genes Oligonucleotide Primers Phytoplankton Ribosomal RNA Genes RNA, Ribosomal, 16S
2
Protein Structural Modeling and Analysis
Cited 12 times
Proteins are modeled as body-and-bar networks where each atom is represented as a body with six degrees of freedom (23 (link)). Interactions between the atoms (covalent and non-covalent bonds) are modeled as a set of bars that restrict internal motion between the atoms. A covalent single bond is modeled with five bars allowing for the dihedral rotation about it; peptide and double bonds are modeled with six bars, disallowing any bond rotation. For example, a diatomic molecule with a single bond, owing to the five constraints, has seven degrees of freedom (6 × 2 − 5), six of which represent the trivial overall rotations and translations and one of which represents the internal rotation around the single bond. Non-covalent interactions, which contribute significantly to protein stability, are also modeled as bars. As such, hydrogen bonds (and salt bridges) are modeled with five bars, whereas hydrophobic and ring stacking interactions are modeled with two and three bars, respectively (24 ). Figure 1A shows the structure of thermolysin-like protease (TLP; PDB code: 1NPC) from which a body-and-bar network is then generated (Figure 1B).

Covalent and non-covalent interactions in a protein structure (A) are modeled as bars in a body-and-bar network (B). A rigid cluster decomposition is carried out for all network states during a thermal unfolding simulation (C) and then post-processed to calculate flexibility indices, phase transitions, and weak spots. (D) Submission page to the CNA web server.

Krüger D.M., Rathi P.C., Pfleger C, & Gohlke H. (2013). CNA web server: rigidity theory-based thermal unfolding simulations of proteins for linking structure, (thermo-)stability, and function. Nucleic Acids Research, 41(Web Server issue), W340-W348.
Publication 2013
Debility Diatoms Exanthema Human Body Hydrogen Bonds Muscle Rigidity Peptides Phase Transition Proteins single bond Sodium Chloride Staphylococcal Protein A thermolysin-like protease
3
Transcriptome Annotation and Assembly Evaluation
Cited 10 times
Transcriptomes were annotated using the dammit pipeline (version v1.0.dev0) [49 ], which relies on the following databases as evidence: Pfam-A (version 28.0) [50 (link)], Rfam (version 12.1) [51 (link)], and OrthoDB (version 8) [52 (link)]. In the case where there were multiple database hits, one gene name per contig was selected by choosing the name of the lowest e-value match (<1e-05).
All assemblies were evaluated using metrics generated by the Transrate program (v1.0.3) [53 (link)]. Trimmed reads were used to calculate a Transrate score for each assembly, which represents the geometric mean of all contig scores multiplied by the proportion of input reads providing positive support for the assembly [50]. Comparative metrics were calculated using Transrate for each MMETSP sample between DIB and the NCGR assemblies using the Conditional Reciprocal Best Basic Local Alignment Search Tool hits (CRBB) algorithm [54 (link)]. A forward comparison was made with the NCGR assembly used as the reference and each DIB re-assembly as the query. Reverse comparative metrics were calculated with each DIB re-assembly as the reference and the NCGR assembly as the query. Transrate scores were calculated for each assembly using the Trimmomatic quality-trimmed reads prior to digital normalization.
Benchmarking Universal Single-Copy Orthologs (BUSCO) software (version 3) was used with a database of 215 orthologous genes specific to protistans and 303 genes specific to eukaryota with open reading frames (ORFs) in the assemblies. BUSCO scores are frequently used as one measure of assembly completeness [55 (link)].
To assess the occurrences of fixed-length words in the assemblies, unique 25-mers were measured in each assembly using the HyperLogLog (HLL) estimator of cardinality built into the khmer software package [56 (link)]. We used the HLL function to digest each assembly and count the number of distinct fixed-length substrings of DNA (k-mers).
Unique gene names were compared from a random subset of 296 samples using the dammit annotation pipeline [49 ]. If a gene name was annotated in NCGR but not in DIB, this was considered a gene uniquely annotated in NCGR. Unique gene names were normalized to the total number of annotated genes in each assembly.
A Tukey’s honest significant different post-hoc range test of multiple pairwise comparisons was used in conjunction with an analysis of variance to measure differences between distributions of data from the top eight most-represented phyla (Bacillariophyta, Dinophyta, Ochrophyta, Haptophyta, Ciliophora, Chlorophyta, Cryptophyta, and Others) using the agricolae package version 1.2-8 in R version 3.4.2 (2017-09-28). Margins sharing a letter in the group label are not significantly different at the 5% level (refer to Fig. 8). Averages are reported ± standard deviation.
Johnson L.K., Alexander H, & Brown C.T. (2018). Re-assembly, quality evaluation, and annotation of 678 microbial eukaryotic reference transcriptomes. GigaScience, 8(4), giy158.
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Publication 2018
Chlorophyta Ciliophora Cryptophyta Diatoms Dinoflagellates Eukaryota Genes GPER protein, human Haptophyta Open Reading Frames Transcriptome
4
Karyotyping of Macrostomum Flatworm Species
Cited 10 times
We analyzed the karyotype of specimens from four related species of the genus Macrostomum: M. lignano, M. sp. 8, M. spirale, and M. hystrix, including seven different inbred lines and outbred cultures within M. lignano, our primary experimental model organism. In Table 1 we summarize the year and site of collection and the culture conditions for the different lines and cultures and in Fig 1 we show the collection sites of the different Macrostomum species and specimens. Sampling in the San Rossore Regional Park was performed under permit 3299/7-2-1 of the Tenuta di San Rossore, and all other sites did not include national parks or other protected areas of land or sea. Moreover, none of the field collections represent collections of endangered or protected species, samples were taking with minimal impact on the studied habitats, and the sampling did not include any vertebrates or cephalopods.
All worms were kept in the laboratory at 20°C, and a light:dark cycle of 14:10 h, and fed with the diatom Nitzschia curvilineata, as previously described for M. lignano [1 , 19 ].
The M. lignano inbred line DV1 has been widely used in different studies, including the recently published M. lignano genome project [12 (link)]. DV1 was created via full-sib and half-sib inbreeding for 24 generations, and has since been kept at small population sizes to maintain a high level of homozygosity [14 (link)]. DV1 was used to create a stable transgenic GFP(+) line, HUB1 [14 (link), 15 , 17 (link)]. Briefly, transgenesis was established by microinjecting the Minos transposon system into single-cell stage eggs. The reporter construct (PEfa::EGFP) contained the sequence of the Minos transposon, the promoter of the housekeeping gene elongation factor 1 alpha (Efalpha), and EGFP (encoding the enhanced GFP protein). After 48–72 h the injected embryos were screened for EGFP expression. Three injected eggs gave rise to three EGFP-transgenic lines and one of them became the HUB1 line [15 ].
In contrast to inbred lines, outbred M. lignano cultures are kept in a metapopulation structure in order to maximize the retention of genetic variability [17 (link)]. Freshly field-collected specimens (Table 1) of M. lignano and of a currently unnamed species of Macrostomum (here referred to as Macrostomum sp. 8, which recent molecular phylogenetic analyses have identified as a sibling species of M. lignano; T. Janssen and L. Schärer, unpublished data) were kept under laboratory conditions for up to two weeks after collection, before karyotype analyses.
More limited analyses were done on specimens from laboratory cultures of two more distantly related Macrostomum species (Table 1). According to the current molecular phylogeny of the genus Macrostomum, both M. spirale and M. hystrix fall into the same subclade of the genus (clade 2 in [13 (link)]; see also there for notes on the taxonomic status of these species names), with M. hystrix being considerably closer to M. lignano than M. spirale.
Zadesenets K.S., Vizoso D.B., Schlatter A., Konopatskaia I.D., Berezikov E., Schärer L, & Rubtsov N.B. (2016). Evidence for Karyotype Polymorphism in the Free-Living Flatworm, Macrostomum lignano, a Model Organism for Evolutionary and Developmental Biology. PLoS ONE, 11(10), e0164915.
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Publication 2016
Animals, Transgenic Cephalopoda Diatoms Eggs Elongation Factor 1alpha Embryo Genome Helminths Homozygote Hystrix Jumping Genes Karyotype Promoter, Genetic Proteins Reproduction Specimen Collection Vertebrates
5
Comparative Genomic Analysis of Oomycete Gene Families
Cited 10 times
In order to find substantial expansions and contractions of gene families observed in other eukaryotes, we used the PANTHER Classification System [49 (link),137 (link),138 ]. We first scored all predicted proteins from the P. ultimum genome against the PANTHER HMMs, and created a tab-delimited file with two columns: the P. ultimum protein identifier and the PANTHER HMM identifier from the top-scoring HMM (if E-value < 0.001). We created similar files for three Phytophthora genomes (Ph. infestans, Ph. ramorum, and Ph. sojae), and a diatom genome (P. tricornutum) for comparison. We removed protein families of probable viral origin or transposons (PTHR19446, PTHR10178, PTHR11439, PTHR23022, PTHR19303). This left 7,762 P. ultimum proteins in PANTHER families, 8,169 from Ph. infestans, 7,667 from Ph. ramorum and 7,701 from Ph. sojae. We then uploaded the tab-delimited files to the PANTHER Gene List Comparison Tool [137 (link),139 ] and analyzed the list for under- and over-representation of genes with respect to molecular functions, biological processes, and pathways. For each class that was significantly different (Bonferroni-corrected P < 0.05) between P. ultimum and all of the Phytophthora genomes, we determined the protein family expansions or contractions that made the biggest contributions to these differences (Table 1). Finally, we determined likely gene duplication and loss events that generated the observed protein family expansions and contractions by building phylogenetic trees of each of these families using the 48 genomes included in the trees on the PANTHER website [140 ], in addition to the five stramenopile genomes above (P. ultimum, Ph. infestans, Ph. ramorum, Ph. sojae, P. tricornutum). Phylogenetic trees were constructed using the GIGA algorithm [141 (link)], which infers the timing of likely gene duplication events relative to speciation events, allowing the reconstruction of ancestral genome content and lineage-specific duplications and losses. Using v3 of the annotation (MAKER output without manual curation), P. ultimum genes orthologous to genes in Ph. infestans, Ph. sojae and Ph. ramorum were identified using PHRINGE ('Phylogenetic Resources for the Interpretation of Genomes') [103 ] in which the evolutionary relationships among all oomycete protein families are reconstructed.
Lévesque C.A., Brouwer H., Cano L., Hamilton J.P., Holt C., Huitema E., Raffaele S., Robideau G.P., Thines M., Win J., Zerillo M.M., Beakes G.W., Boore J.L., Busam D., Dumas B., Ferriera S., Fuerstenberg S.I., Gachon C.M., Gaulin E., Govers F., Grenville-Briggs L., Horner N., Hostetler J., Jiang R.H., Johnson J., Krajaejun T., Lin H., Meijer H.J., Moore B., Morris P., Phuntmart V., Puiu D., Shetty J., Stajich J.E., Tripathy S., Wawra S., van West P., Whitty B.R., Coutinho P.M., Henrissat B., Martin F., Thomas P.D., Tyler B.M., De Vries R.P., Kamoun S., Yandell M., Tisserat N, & Buell C.R. (2010). Genome sequence of the necrotrophic plant pathogen Pythium ultimum reveals original pathogenicity mechanisms and effector repertoire. Genome Biology, 11(7), R73.
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Publication 2010
Biological Evolution Biological Processes Diatoms Eukaryota Gene Duplication Genes Genome Hypertelorism, Severe, With Midface Prominence, Myopia, Mental Retardation, And Bone Fragility Jumping Genes OCA2 protein, human Oomycetes Phytophthora Reconstructive Surgical Procedures Reproduction Stramenopiles Trees Viral Proteins

Most recents protocols related to «Diatoms»

1
Diatom DNMT-like Sequence Identification
We performed an extensive scan of the MMETSP database, enriched with 7 diatom transcriptomes and genomes from the top 20 most abundant diatoms found in Tara Oceans database62 (link), using HMMER-search with the model PF00145 to fetch any DNMT-like, including partial transcripts, sequence within microeukaryotes. We ran HMMER in a non-stringent fashion to not miss positives DNMT sequences. We used eDAF approach to filter the expected high number of false positives. It is worth noting that we initially use HMMER for screening instead of the built-in module of eDAF due to the time complexity of the latter for extensive searches (tens to hundreds of times slower than HMMER). Reciprocal BLAST best hit analysis was performed as previously described63 (link). Briefly, the DNMT3 (Phatr3_J47136), DNMT4 (Thaps3_11011), DNMT5 (Phatr3_EG02369) and DNMT6 (Phatr3_J47357) orthologues found in P. tricornutum or T. pseudonana (for DNMT4) were blasted against a phylogenetically optimized database that include MMETSP transcriptomes. Putative DNMT sequence hits giving back the corresponding enzyme (DNMT3, DNMT4, DNMT5, or DNMT6) at the threshold of e-value of 1 × 10−5 in the corresponding diatom were retained. Candidate enzymes were then analyzed using eDAF.
Hoguin A., Yang F., Groisillier A., Bowler C., Genovesio A., Ait-Mohamed O., Vieira F.R, & Tirichine L. (2023). The model diatom Phaeodactylum tricornutum provides insights into the diversity and function of microeukaryotic DNA methyltransferases. Communications Biology, 6, 253.
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Publication 2023
Diatoms DNA Modification Methylases Enzymes Genome Radionuclide Imaging Toxic Epidermal Necrolysis Transcriptome
2
Generating CRISPR/Cas9 Knockouts in Phaeodactylum tricornutum
The CRISPR/Cas9 knockouts were performed as previously described44 (link). Our strategy consisted in the generation of short deletions and insertions to disrupt the open reading frame of putative DNMTs of P. tricornutum. We introduced by biolistic the guide RNAs independently of the Cas9 and ShBle plasmids, conferring resistance to Phleomycin, into the reference strain Pt1 8.6 (referred hereafter as ‘reference line’ or ‘wild-type’- WT). Briefly, specific target guide RNAs were designed in the first exon of Phatr3_EG02369 (DNMT5), Phatr3_J47357 (DNMT6) and Phatr3_J36137 (DNMT3) using the PHYTO/CRISPR-EX66 (link) software and cloned into the pU6::AOX-sgRNA plasmid by PCR amplification. For PCR amplification, plasmid sequences were added in 3’ of the guide RNA sequence (minus –NGG), which are used in a PCR reaction with the template pU6::AOX-sgRNA. Forward primer – sgRNA seq + GTTTTAGAGCTAGAAATAGC. Reverse primer - sequence to add in 3’ reverse sgRNA seq + CGACTTTGAAGGTGTTTTTTG. This will amplify a new pU6::AOX-(your_sgRNA). The PCR product is digested by the enzyme DPN1 (NEB) in order to remove the template plasmid and cloned in TOPO10 E. coli. The sgRNA plasmid, the pDEST-hCas9-HA and the ShBLE Phleomycin resistance gene cloned into the plasmid pPHAT-eGFP were co-transformed by biolistic in the Pt1 8.6 ‘Wild Type’ strain as described in44 (link). We also generated a cell line that was transformed with pPHAT-eGFP and pDEST-hCas9-HA but no guide RNAs. This line is referred as the Cas9:Mock line.
CRISPR/Cas9—sgRNA transformants were selected by phleomycin resistance (carried by the plasmid pPHAT-eGFP). 48 hours post-transformation, diatoms were replated and grown on phleomycin 100 µg/ml 50% Enhanced Artificial Sea Water67 (link) plates until single colonies appeared (2–3 weeks). Transformants were isolated from plates and sanger sequenced after PCR using HGS Diamond Taq® as per manufacturer instruction and the primers DNMT5locus_R and DNMT5locus_F (Supplementary Data 19). These primers allow the amplification and sequencing of both alleles of DNMT5. Sequencing using the primer DNMT5locus_R shows that mutants are homozygous for the deletions. In addition, all mutants show loss of heterozygosity (LOH).
Hoguin A., Yang F., Groisillier A., Bowler C., Genovesio A., Ait-Mohamed O., Vieira F.R, & Tirichine L. (2023). The model diatom Phaeodactylum tricornutum provides insights into the diversity and function of microeukaryotic DNA methyltransferases. Communications Biology, 6, 253.
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Publication 2023
Alleles Biolistics Cell Lines Clustered Regularly Interspaced Short Palindromic Repeats Diamond Diatoms DNA Modification Methylases Enzymes Escherichia coli Exons Gene Deletion Genes Homozygote Insertion Mutation Oligonucleotide Primers Phleomycins Plasmids RNA Strains
3
Biogeochemical Characterization of Antarctic Polynyas
Sampling was performed on board of the RSV Aurora Australis (AU1602, AA-V02 2016/17) between 8 December 2016 and 21 January 2017, in one station belonging to the Dalton polynya (D02) and two stations (M36 and M48) located within the Mertz polynya (East Antarctica; 67.2–66.8 °S and 119.5–145.8°E; Supplementary Figure S1A). These stations were chosen due to their contrasting biological, chemical, and physical characteristics, extensively described and discussed by Moreau et al. (2019) (link) and Ratnarajah et al. (2022) (link). For comparison with Ratnarajah et al. (2022) (link), D02 refers to St.2; M36 is EM03 and M48 is MG08. Briefly, both polynyas had similar sea surface temperatures ranging majorly between 0.0 and 1.5°C, but the Dalton polynya showed higher sea surface salinity than the Mertz polynya (34.0–34.3 g/kg vs. 32.5–33.5 g/kg, respectively), suggesting that the later could have experienced more sea ice melting than the Dalton polynya (Moreau et al., 2019 (link)). The Dalton polynya presented deeper euphotic depths (95 ± 56 m) and mixed layer depths (25 ± 12 m, excluding two stations where the mixed layer was down to 100 m and 154 m) than the Mertz polynya (40 ± 9 m and 13 ± 1 m, respectively) (Ratnarajah et al., 2022 (link)). In general, chlorophyll-a (Chl-a) concentrations in the surface were higher in the Dalton polynya compared to the Mertz polynya (max of 15 μg L−1 vs. 8 μg L−1), but Mertz presented a subsurface Chl-a maximum of ~10 μg L−1 located between 20 and 70 m depth that was consistent along the whole polynya, whereas in the Dalton that layer was more variable (Moreau et al., 2019 (link)). Both polynyas also presented global differences in nutrient ratios (i.e., Si:N or N:P) suggesting different nutrient sources (i.e., water masses) and different phytoplanktonic communities, something which was also corroborated by microscope analyses (Moreau et al., 2019 (link)). During the time of sampling, the dominant phytoplankton groups differed between both polynyas, with Phaeocystis antarctica dominating in the Dalton station and diatoms dominating in the much more productive Mertz stations (Moreau et al., 2019 (link)).
The sampling and analyses of physicochemical and environmental parameters were performed as described in Moreau et al. (2019) (link) and Ratnarajah et al. (2022) (link). Temperature and salinity were obtained from the CTD (Rosenberg and Rintoul, 2017 ) and fluorescence values were obtained with a fluorometer (ECO-AFL/FL 756, Wetlabs, United States) that was installed also on the CTD rosette. Of particular interest for this study is the concentration of Chl-a, particulate organic carbon (POC) and inorganic nutrients. Chl-a concentrations were obtained from ~500 ml of filtered seawater, extracted with acetone and stored at −20°C for 24–48 h in the dark prior to analysis with a Turner Trilogy fluorometer. POC was determined following Knap et al. (1996) and analyzed using a Thermo Finnigan EA 1112 Series Flash Elemental Analyzer. Concentrations of inorganic macronutrients (nitrate, nitrite and silicic acid) were analyzed after the voyage at the CSIRO laboratory (Hobart, Australia) following the methods described in Murphy and Riley (1962) (link), Armstrong et al. (1967) (link), Wood et al. (1967) (link) and Kérouel and Aminot (1997) (link).
Puigcorbé V., Ruiz-González C., Masqué P, & Gasol J.M. (2023). Impact of particle flux on the vertical distribution and diversity of size-fractionated prokaryotic communities in two East Antarctic polynyas. Frontiers in Microbiology, 14, 1078469.
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Publication 2023
A-A-1 antibiotic Acetone Biopharmaceuticals Carbon chlorophyll a' Diatoms Fluorescence Macronutrient Microscopy Nitrates Nitrites Nutrients Phaeocystis Physical Examination Phytoplankton Salinity Sea Ice Cover Silicic acid
4
Determining Microalgal Growth and Biomass
Algal growth in terms of optical density at 680 nm (O.D.680 nm) using a Spectrophotometer (DU 730, Beckman Coulter, Brea, CA, USA) and dry cell weight (DCW, g/L) was evaluated in duplicates for each algal species21 (link),22 (link). Every 24 h the O.D. was measured for each of the algae to derive a growth curve. Nitrate (NO3−), nitrite (NO2−), phosphate (PO4−), and pH were measured every 24 h using test strips (Hach Company, Loveland, Colorado, USA, and Micro Essential Laboratory (HYDRION), Brooklyn, New York, USA). At the end of the cultivation, the algal cells were harvested by centrifuging at 5000 g for 10 min at 25 °C and washed twice with distilled water (C. vulgaris) or 0.9% NaCl solution (N. oculata and C. cryptica) to remove media components. The resultant biomass was then placed in a 50 °C-oven to dry overnight. The DCW was gravimetrically determined using a benchtop digital scale (Mettler Toledo, USA) in accordance with the following equation: DCW(g)=Postovendriedbiomass+vialg-Pre - ovendriedvialweightg.
DCW values are typically used directly in microalgal species containing ash levels < 10%. However, diatoms typically contain considerable mineral ash because of the contribution of the non-carbonaceous silica exoskeleton23 (link), although even green algae can exhibit ash content of 10% or more24 . As a result, in species with higher ash percentages, the Ash Free Dry Weight (AFDCW; g/L) was determined to ensure accurate measurement of the concentration and productivity of individual biochemical constituents, since compositional analysis solely based on DCW measurements can underestimate the actual amount25 (link). For AFDCW measurement, dry biomass was added to pre-weighed aluminum weigh pans and then reweighed. The pans were then placed in a muffle oven and oxidized (ashed) at 475 °C for 5 h. After cooling, the samples were reweighed and recorded. AFDCW% was calculated in accordance with the following equation: AFDCW%=100×Pre-combustionbiomass+pang-Post-combustionbiomass+pang/Pre-combustionbiomassg.
Because of the aforementioned high-ash concerns, AFDCW was used throughout this study and determined in accordance with the following equation: AFDCWg=DCWg∗1-AFDCW%/100.
Merz C.R., Arora N., Welch M., Lo E, & Philippidis G.P. (2023). Microalgal cultivation characteristics using commercially available air-cushion packaging material as a photobioreactor. Scientific Reports, 13, 3792.
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Publication 2023
Aluminum Cells Chlorophyta Diatoms Fingers Microalgae Minerals Nitrates Nitrites Normal Saline Phosphates Silicon Dioxide
5
Phytoplankton Sampling and Quantification
Three water samples were collected (using 1 L polyethylene bottles) at each sampling site and fixed in situ with 15 mL of 5% Lugol’s iodine solution. After settlement for 48 h, the supernatant of preserved phytoplankton samples was slowly siphoned with a thin rubber tube, leaving 30 mL of settled phytoplankton and were transferred into 50 mL sample bottles. The polyethylene bottles were rinsed two or three times with the supernatant, and the phytoplankton sample volume was then brought to about 50 ml in the sample bottles. Finally, 1–2 mL of 40% formaldehyde was added to samples for long-term preservation (Ding et al., 2022 (link)). A 0.1-mL subsample was transferred to the Sedgewick Rafter counting chamber and phytoplankton density was examined under an inverted microscope (Zeiss, Jena, Germany). We observed 100 random fields at 400× magnification, and each sample was examined three times. For diatoms, permanent slides were prepared following Wu et al. (2021) (link) and inspected with an optical microscope at 1,000× magnification. The identification and quantification of phytoplankton species were according to the methodology described by Hu and Wei (2006) based on the morphological structure and habitat type of phytoplankton. Phytoplankton was identified to the lowest taxonomic level possible. The phytoplankton biomass of each sample was estimated using phytoplankton biovolume. We randomly selected 30–50 individuals of each specie and measured their length, height and diameter according to the most approximate geometric shape to calculate the average biovolume (Hillebrand et al., 1999 (link)). The average biovolume and abundance-based results of each species were used to estimate biomass using the conversion factor (1 μm3 = 1 pg) (Wetzel and Likens, 2000 ).
Huang Z., Pan B., Soininen J., Liu X., Hou Y, & Liu X. (2023). Seasonal variation of phytoplankton community assembly processes in Tibetan Plateau floodplain. Frontiers in Microbiology, 14, 1122838.
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Publication 2023
Biologic Preservation Diatoms Fibrinogen Formaldehyde Iodine Light Microscopy Lugol's solution Microscopy Phytoplankton Polyethylene, High-Density Rubber

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4
Trizol reagent by Thermo Fisher Scientific
Sourced in United States, China, Japan, Germany, United Kingdom, Canada, France, Italy, Australia, Spain, Switzerland, Netherlands, Belgium, Lithuania, Denmark, Singapore, New Zealand, India, Brazil, Argentina, Sweden, Norway, Austria, Poland, Finland, Israel, Hong Kong, Cameroon, Sao Tome and Principe, Macao, Taiwan, Province of China, Thailand
TRIzol reagent is a monophasic solution of phenol, guanidine isothiocyanate, and other proprietary components designed for the isolation of total RNA, DNA, and proteins from a variety of biological samples. The reagent maintains the integrity of the RNA while disrupting cells and dissolving cell components.
5
Abi prism 3100 avant by Thermo Fisher Scientific
Sourced in United States
The ABI PRISM 3100-Avant is a capillary electrophoresis genetic analyzer designed for DNA sequencing and fragment analysis. It features 16 capillaries and utilizes four-color fluorescence detection technology for high-throughput genetic analysis.
6
Axio scope a1 microscope by Zeiss
Sourced in Germany, United States, United Kingdom, China
The Axio Scope A1 microscope is a compact and versatile light microscope designed for routine laboratory applications. It features a stable stand and a range of optical components that support various illumination techniques, including brightfield, darkfield, and phase contrast. The Axio Scope A1 is suitable for a variety of sample types and can be used in educational, clinical, and industrial settings.
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Axiovert 25 by Zeiss
Sourced in Germany, United States, Italy, Switzerland, United Kingdom
The Axiovert 25 is an inverted microscope designed for routine laboratory use. It provides basic functionality for observation and documentation of biological samples.
8
Axio vert a1 by Zeiss
Sourced in Germany, United States
The Axio Vert.A1 is a high-performance inverted microscope designed for a wide range of applications in life science research. It features a stable and vibration-free optical system, providing excellent image quality and resolution. The microscope is equipped with various illumination options and supports a variety of observation techniques, making it a versatile tool for researchers and scientists.
9
Sybr green 1 by Thermo Fisher Scientific
Sourced in United States, Germany, Japan, United Kingdom, Switzerland, China, Australia, Canada, Belgium, France
SYBR Green I is a fluorescent dye used in molecular biology for the detection and quantification of DNA. It binds to double-stranded DNA and emits a strong fluorescent signal upon excitation, allowing for the sensitive and specific measurement of DNA levels in various applications such as real-time PCR and DNA gel electrophoresis.
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Phusion high fidelity dna polymerase by Thermo Fisher Scientific
Sourced in United States, Germany, Finland, Lithuania, France, United Kingdom, Spain, Canada, Switzerland, Sweden, Belgium
Phusion High-Fidelity DNA Polymerase is a thermostable DNA polymerase engineered for high-fidelity DNA amplification. It possesses 3'→5' exonuclease proofreading activity, resulting in an error rate up to 50-fold lower than Taq DNA polymerase.

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