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Cfx384 thermocycler

Manufactured by Thermo Fisher Scientific
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The CFX384 thermocycler is a high-performance real-time PCR instrument designed for accurate temperature control and precise thermal cycling. It features a 384-well block and can accommodate a wide range of sample volumes for efficient nucleic acid amplification and detection.

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Lab products found in correlation

Sybrselect for cfx master mix, by Thermo Fisher Scientific (2 mentions) Forskolin, by Bio-Techne (2 mentions) Quantitect rt pcr kit, by Qiagen (2 mentions) Taqman primer, by Thermo Fisher Scientific (2 mentions) Rneasy kit, by Qiagen (2 mentions)

4 protocols using cfx384 thermocycler

1

Quantitative PCR of 16S rRNA genes

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Quantitative PCR (qPCR) was performed on 216/290 samples that were available from the 16S rRNA gene sequencing cohort. qPCR of total 16S rRNA gene copies was carried out in triplicate 10 μL reactions with 200 nM 340F/514R primers using a BioRad CFX384 thermocycler with SYBRSelect for CFX Master Mix (Life Technologies) according to the manufacturer’s instructions and an annealing temperature of 60 °C. Absolute quantifications were determined based against a standard curve of purified 8F/1542R amplified E. coli MG1655 DNA42 . Reactions were performed in triplicate and mean values were taken for further downstream analyses. Absolute bacterial abundance was derived by adjustments for dilutions during DNA extraction, normalization, and PCR reaction preparation dividing by the total fecal mass used for DNA extraction in grams.
von Schwartzenberg R.J., Bisanz J.E., Lyalina S., Spanogiannopoulos P., Ang Q.Y., Cai J., Dickmann S., Friedrich M., Liu S.Y., Collins S.L., Ingebrigtsen D., Miller S., Turnbaugh J.A., Patterson A.D., Pollard K.S., Mai K., Spranger J, & Turnbaugh P.J. (2021). Caloric restriction disrupts the microbiota and colonization resistance. Nature, 595(7866), 272-277.
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2

Quantifying Gut Bacterial Abundance

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Quantitative PCR (qPCR) was performed on DNA extracted from the human stool samples. DNA templates were diluted to 5 ng/µL before qPCR of total 16S rRNA gene copies was carried out in triplicate 10 µL reactions with 200 nM 340F/514R primers using a BioRad CFX384 thermocycler with SYBR Select for CFX Master Mix (Life Technologies) according to the manufacturer's instructions and an annealing temperature of 60˚C. Absolute quanti cations were determined against a standard curve of puri ed 8F/1542R ampli ed Akkermansia muciniphila DNA. Mean values of triplicate reactions were taken for further downstream analyses. Absolute bacterial abundance was derived by adjustments for dilutions during DNA extraction and template normalization dividing by the total fecal mass used for DNA extraction in grams.
Ang Q.Y., Alba D., Upadhyay V., Bisanz J.E., Cai J., Lee H.L., Barajas E., Wei G., Patterson A.D., Koliwad S.K., & Turnbaugh P.J. (2020). Differences in the Gut Microbiomes of Distinct Ethnicities Within the Same Geographic Area Are Linked to Host Metabolic Health.
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3

Rhythmic Gene Expression Analysis in Neural Cells

Cited 1 time
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For gene expression analyses, NPCs (n=2 control, 2 Li-R, 2 Li-NR) or neurons (n=2 control, 2 Li-R, 3 Li-NR) were grown to a density of 400×103/well. Cells were synchronized using 10 μM forskolin (Tocris). After 16 h, plates were collected at 6 h intervals over 24 h. Plates were washed with cold PBS and frozen at −80°C. RNA was extracted using RNeasy kit (Qiagen) and quantified by spectrophotometer. RNA (500 ng) was reverse transcribed into cDNA using QuantiTect RT-PCR kit (Qiagen). RT-PCR was performed on a BioRad CFX384 thermocycler using pre-validated Taqman primers (Applied Biosystems) for BMAL1 (ARNTL), CLOCK, CRY1, NR1D1, PER1/2/3, and RORA. Expression was normalized to GAPDH, a non-rhythmic reference gene18 (link) and normalized to mean expression over 24 h. Using data matched for sample and experiment, correlation coefficient across time for each gene pair were calculated to identify network-level patterns in expression. Correlation coefficients were z-transformed and network connectivity was visualized using Cytoscape19 (link) and analyzed by 2-way ANOVA.
Mishra H.K., Ying N.M., Luis A., Wei H., Nguyen M., Nakhla T., Vandenburgh S., Alda M., Berrettini W.H., Brennand K.J., Calabrese J.R., Coryell W.H., Frye M.A., Gage F.H., Gershon E.S., McInnis M.G., Nievergelt C.M., Nurnberger J.I., Shilling P.D., Oedegaard K.J., Zandi P.P., Kelsoe J.R., Welsh D.K, & McCarthy M.J. (2021). Circadian rhythms in bipolar disorder patient-derived neurons predict lithium response: Preliminary studies. Molecular psychiatry, 26(7), 3383-3394.
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4

Rhythmic Gene Expression Analysis in Neural Cells

Cited 1 time
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For gene expression analyses, NPCs (n=2 control, 2 Li-R, 2 Li-NR) or neurons (n=2 control, 2 Li-R, 3 Li-NR) were grown to a density of 400×103/well. Cells were synchronized using 10 μM forskolin (Tocris). After 16 h, plates were collected at 6 h intervals over 24 h. Plates were washed with cold PBS and frozen at −80°C. RNA was extracted using RNeasy kit (Qiagen) and quantified by spectrophotometer. RNA (500 ng) was reverse transcribed into cDNA using QuantiTect RT-PCR kit (Qiagen). RT-PCR was performed on a BioRad CFX384 thermocycler using pre-validated Taqman primers (Applied Biosystems) for BMAL1 (ARNTL), CLOCK, CRY1, NR1D1, PER1/2/3, and RORA. Expression was normalized to GAPDH, a non-rhythmic reference gene18 (link) and normalized to mean expression over 24 h. Using data matched for sample and experiment, correlation coefficient across time for each gene pair were calculated to identify network-level patterns in expression. Correlation coefficients were z-transformed and network connectivity was visualized using Cytoscape19 (link) and analyzed by 2-way ANOVA.
Mishra H.K., Ying N.M., Luis A., Wei H., Nguyen M., Nakhla T., Vandenburgh S., Alda M., Berrettini W.H., Brennand K.J., Calabrese J.R., Coryell W.H., Frye M.A., Gage F.H., Gershon E.S., McInnis M.G., Nievergelt C.M., Nurnberger J.I., Shilling P.D., Oedegaard K.J., Zandi P.P., Kelsoe J.R., Welsh D.K, & McCarthy M.J. (2021). Circadian rhythms in bipolar disorder patient-derived neurons predict lithium response: Preliminary studies. Molecular psychiatry, 26(7), 3383-3394.
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