COMPARISON OF TWO TOTAL RNA EXTRACTION PROTOCOLS FROM CHO-K1 CELLS FOR RT-PCR: CUT-OFF COST FOR RESEARCHERS
Bibliographic record
Abstract
ABSTRACT: Various methods have been described to extract RNA from adherent mammalian cells. RNA isolation in conjunction with reverse transcription polymerase chain reaction (RT-PCR) is a valuable tool used to study gene expression profiling. This approach is now being used in mammalian cell bioprocessing to help understand and improve the system. The objective of this study was to compare and determine the most suitable RNA extraction method for CHO-K1 cells in a setting where a relatively large amount of samples was involved. Total RNA was extracted using Total RNA purification kit (without DNase treatment; Norgen, Canada) and RNeasy mini kit (with DNase treatment; Qiagen, USA) respectively. The extracted RNA was then reverse transcribed, and the cDNA was subjected to PCR-amplifying 18S. Yield from RNeasy kit was significantly higher (0.316 ± 0.033 µg/µl; p=0.004) than Total RNA purification kit (0.177 ± 0.0243 µg/µl). However, RNA purity for both methods was close to 2.0 and there was no significant difference between the methods. Total RNA purification kit is less expensive than RNeasy kit. Since there is no DNase treatment step in the former, extraction time for RNA is shorter. When the extracted RNA was subjected to RT-PCR, both methods were able to show detection of 18S at 219 bp.  Therefore, this study demonstrates that both protocols are suitable for RNA extraction for CHO-K1 cells. RNeasy mini kit (Qiagen) is recommended if higher yields is the primary concern and Total RNA Purification kit (Norgen) is recommended if time and cost are concerned. ABSTRAK: Pelbagai kaedah telah digunakan untuk mengekstrak RNA daripada sel mamalia lekat. Pemencilan RNA dengan menggunakan reaksi rantai polimerase transkripsi berbalik (RT-PCR) merupakan kaedah penting yang digunakan dalam mengkaji pernyataan gen berprofil. Pendekatan ini kini digunakan dalam pemprosesan bio sel mamalia untuk memahami dan menambah baik sistem. Tujuan kajian dijalankan adalah untuk menentukan dan membandingkan kaedah ekstraksi RNA yang paling sesuai bagi sel CHO-K1 di persekitaran di mana kadar sampel yang agak besar terlibat. Jumlah RNA diekstrak menggunakan kit penulenan Jumlah RNA (tanpa rawatan DNase; Norgen, Canada) dan kit mini RNeasy (dengan rawatan DNase; Qiagen, USA). RNA yang diekstrak kemudiannya diterbalikkan transkripsi, dan cDNA menjalani penguat PCR 18S. Hasil daripada kit RNeasy adalah lebih tinggi (0.316 ± 0.033 µg/µl; p=0.004) berbanding dengan kit penulenan Jumlah RNA (0.177 ± 0.0243 µg/µl). Walaupun begitu, kaedah penulenan RNA untuk kedua-duanya hampir 2.0 dan tidak terdapat perbezaan yang ketara antara keduanya. Kit penulenan Jumlah RNA adalah lebih murah berbanding dengan kit RNeasy. Memandangkan tidak ada langkah rawatan DNase dengan penggunaan kit Jumlah RNA, tempoh ekstrak RNA nya lebih pendek. Apabila RNA yang telah diekstrak menjalani RT-PCR, kedua-dua kaedah berjaya mengesan 18S pada 219 bp.  Kesimpulannya, kajian ini menunjukkan kedua-dua kaedah sesuai untuk mengekstrak RNA bagi sel CHO-K1. Kit mini RNeasy (Qiagen) lebih sesuai jika hasil yang tinggi diinginkan dan kit penulenan Jumlah RNA (Norgen) pula ideal, jika kos dan masa berkepentingan.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".