Mark A. Edwards, Rhonda A. Loxley, Kellysan Powers-Martin, Janusz Lipski, Douglas J. McKitrick, Leonard F. Arnolda, Jacqueline K. Phillips
Molecular Brain Research 129 (2004) 33-43
NMDA receptors are made up of one NR1 subunit and at least one NR2A-D subunit. NR1 (of which there are several splice variants) is required for a functioning NMDA receptor, but none of the NR2 subunits are. Different splice variants and NR2 subunits can be combined to produce NMDA receptors with different pharmacological properties. SHR rats are known to be more sensitive to glutamate in the RVLM, and the authors of this paper are interested in seeing if the SHRs have different numbers or types of NDMA receptors than the WKYs do. Also of interest is the action of NO, which is involved in regulating sympathetic output from the RVLM. NO is produced by three different enzymes: neuronal NO synthase (nNOS), inducible NO synthase (iNOS), and endothelial NO synthase (eNOS), which have different stimuli (and thus different amounts of activity) and can produce different effects on sympathetic activity. In addition, NMDA receptors can trigger the production of NO by nNOS when activated, and NO can cause modification of the NMDA receptor. Conventional and RT-PCR were used to study the potential differences in gene expression related to these processes between the SHRs and WKYs.
Tissue punches were performed in the RVLM and RNA was isolated. Reverse transcription was performed on the samples to obtain cDNA. Conventional PCR was used to detect NR1 splice variants and these products were sequenced to confirm the presence of specific splice variants. RT-PCR was done with Taqman probes to detect expression of NR1, NR2A-D, nNOS, and iNOS, which were normalized to neuronal specific enolase (NSE), the reference gene they chose. Within each sample, NR2 subunits were compared to NR2A to determine relative abundance, because NR2A did not vary between SHRs and WKYs. Finally, immunohistochemistry was used to detect nNOS in the RVLM.
There was no difference in the presence of NR1 splice variants between the two strains (but these were not quantified). NR1 was found in highest concentration in both rat strains, and along with NR2A and NR2B, did not change significantly in concentration between the them. NR2C had the highest expression among the NR2 subunits, and along with NR2D, was found in lower concentration in the SHRs than in the WKYs (NR2C was expressed only 0.37 times as much, and NR2D was expressed 0.26 times as much). nNOS levels were higher in the WKYs than in the SHRs. Because the composition of subunits affects the receptor's activity, it can be hypothesized that the difference in composition of NDMA receptors in the RVLMs of the two strains may contribute to the difference in sympathetic nerve activity that is seen (which would be a topic for another paper; this one did not address cause and effect).
Showing posts with label Natalie. Show all posts
Showing posts with label Natalie. Show all posts
Wednesday, July 20, 2011
Tuesday, July 19, 2011
Analyzing real-time PCR data by the comparative Ct method
Thomas D Schmittgen and Kenneth J Livak
Nature Protocols Vol. 3 No. 6 1101, 2008
Note: the procedures involved in preparing for and running the PCR are covered in the paper but will not be detailed in this post.
Real-time RT PCR is a method of analyzing gene expression. The RT stands for reverse transcription; RNA is converted to DNA and then amplified by PCR. Specific fluorescent probes allow for detection of the amount of the gene of interest present after each cycle. The number of the cycle during which it reaches the threshold (the red line in the graph below; set at a value below which the noise is too great to give an accurate impression of the actual fluorescence due to DNA quantity), or Ct, depends on the amount of RNA for the target gene that was in the original sample. The cycler will give out the exact Ct for each sample, along with a graph showing the fluorescence of the sample vs. the cycle number, to show how the fluorescence changes over time. An example graph is shown below, with one sample being probed for two different genes. Each gene is assayed in triplicate, so the graph shows six different curves (in two close groups of three).
A lower Ct means more RNA, which means more gene expression. By comparing the Ct of the target gene (VGLUT2, in the above example) to the Ct of a reference gene (a gene that is always expressed in the same amount; HPRT, in the above example), we can come up with a ratio of expression of the target gene to expression of the reference green. If this is done in many different samples, than we can compare those ratios to determine if the samples express the target gene in the same amounts. This depends largely on two things: picking a good reference gene that is known to be equal in all samples and using a reliable mathematical approach to compare the samples.
Schmittgen and Livak spend a good deal of time on this second concern in their paper. The most effective approach for comparing the treatment and control groups depends on what one is attempting to study, but the initial steps are the same. First, average the triplicates; each sample is probed for each gene in triplicate to reduce the effect of pipetting error on the results. If the triplicates are too different (if the standard deviation between the Ct values is more than 0.3), one or all three must be thrown out. If they are within the acceptable range, then the average Ct will be used from here on out.
Secondly, calculate the deltaCt. This is done by subtracting the Ct of the reference gene from the housekeeping gene (see equation below). Do this for every sample. After this, the next step depends on whether the goal is to compare individual samples to each other or to compare groups of samples to each other. Now it becomes important to know the origin or importance of the samples, so I will refer to them as belonging to the treatment group or to the control group.
If the goal is to compare each individual sample from the treatment group to a matched control, subtract the deltaCt of the control sample from the deltaCt of the treatment sample to get a deltadeltaCt (see first equation below). Next raise 2 to the power of -deltadeltaCt to obtain a fold change (see second equation below). For example, if deltadeltaCt is 2, the fold-change in gene expression will be 2^(-2), or 0.25. This means that the target gene is expressed one-fourth as much in the treatment sample than in the control sample. When this is calculated for each pair of treatment and control samples, the fold-change values can be averaged to obtain an average fold-change. Note that when the fold change is less than one, it can be presented as 1/fold change; for example, a 0.25-fold change could be stated as a 4-fold reduction.
Fold change = 2^(-deltadeltaCt)
If the goal is to compare a treatment group to a control group, average the deltaCt values within the groups and calculate the SDs. After this, there will be one treatment deltaCt and SD, and one control deltaCt and SD. The control group deltaCt can be subtracted from the treatment group deltaCt to obtain a deltadeltaCt. This is used as above to obtain a fold-change.
The reason that the second situation requires averaging the Ct values and the first situation does not is that the samples in the second situation are not paired with controls. If you have a clinical study in which you have matched your treatment subjects with controls based on age, weight, or other criteria, this is the first situation. This is quite useful with small studies that have differences between their treatment individuals that could make lumping them together suppress the effects of the treatment (which would happen when averaging blood pressures, for example, when some patients are children and some are adults; matching each child to a control child and each adult to a control adult will control for difference within the group so that differences between the groups will be apparent). However, many studies (especially larger ones) randomize their subjects and end up with two groups but no specific pairs. In these cases, you could pair the subjects after the fact, but it would not be as necessary or as beneficial as it is in the first situation. Therefore, averaging the deltaCts before calculating the deltadeltaCt is the better option.
Alternately, 2^-deltaCt can be calculated for each sample. Those can be averaged for each group, and the fold-change would then be calculated by dividing the mean from the treatment group by the mean from the untreated group (see equation below).
Now, all of this is only valid if the target and reference genes have similar amplification efficiencies, which are influenced by the primers and the PCR conditions. Amplification efficiency is determined by running a PCR plate with different concentrations of the same sample and then plotting the Ct vs. log(concentration). The data points should form a line, with the efficiency equal to 10^(-1/slope). If the efficiencies are not within 10% of each other (and 10% of 2; an amplification efficiency of 2 shows that the DNA doubles each cycle), then the PCR conditions should be altered or new primers should be ordered.
Nature Protocols Vol. 3 No. 6 1101, 2008
Note: the procedures involved in preparing for and running the PCR are covered in the paper but will not be detailed in this post.
Real-time RT PCR is a method of analyzing gene expression. The RT stands for reverse transcription; RNA is converted to DNA and then amplified by PCR. Specific fluorescent probes allow for detection of the amount of the gene of interest present after each cycle. The number of the cycle during which it reaches the threshold (the red line in the graph below; set at a value below which the noise is too great to give an accurate impression of the actual fluorescence due to DNA quantity), or Ct, depends on the amount of RNA for the target gene that was in the original sample. The cycler will give out the exact Ct for each sample, along with a graph showing the fluorescence of the sample vs. the cycle number, to show how the fluorescence changes over time. An example graph is shown below, with one sample being probed for two different genes. Each gene is assayed in triplicate, so the graph shows six different curves (in two close groups of three).
A lower Ct means more RNA, which means more gene expression. By comparing the Ct of the target gene (VGLUT2, in the above example) to the Ct of a reference gene (a gene that is always expressed in the same amount; HPRT, in the above example), we can come up with a ratio of expression of the target gene to expression of the reference green. If this is done in many different samples, than we can compare those ratios to determine if the samples express the target gene in the same amounts. This depends largely on two things: picking a good reference gene that is known to be equal in all samples and using a reliable mathematical approach to compare the samples.
Schmittgen and Livak spend a good deal of time on this second concern in their paper. The most effective approach for comparing the treatment and control groups depends on what one is attempting to study, but the initial steps are the same. First, average the triplicates; each sample is probed for each gene in triplicate to reduce the effect of pipetting error on the results. If the triplicates are too different (if the standard deviation between the Ct values is more than 0.3), one or all three must be thrown out. If they are within the acceptable range, then the average Ct will be used from here on out.
Secondly, calculate the deltaCt. This is done by subtracting the Ct of the reference gene from the housekeeping gene (see equation below). Do this for every sample. After this, the next step depends on whether the goal is to compare individual samples to each other or to compare groups of samples to each other. Now it becomes important to know the origin or importance of the samples, so I will refer to them as belonging to the treatment group or to the control group.
deltaCt = Ct(target gene) - Ct(reference gene)
If the goal is to compare each individual sample from the treatment group to a matched control, subtract the deltaCt of the control sample from the deltaCt of the treatment sample to get a deltadeltaCt (see first equation below). Next raise 2 to the power of -deltadeltaCt to obtain a fold change (see second equation below). For example, if deltadeltaCt is 2, the fold-change in gene expression will be 2^(-2), or 0.25. This means that the target gene is expressed one-fourth as much in the treatment sample than in the control sample. When this is calculated for each pair of treatment and control samples, the fold-change values can be averaged to obtain an average fold-change. Note that when the fold change is less than one, it can be presented as 1/fold change; for example, a 0.25-fold change could be stated as a 4-fold reduction.
deltadeltaCt = deltaCt(treatment group) - deltaCt(control group)
Fold change = 2^(-deltadeltaCt)
If the goal is to compare a treatment group to a control group, average the deltaCt values within the groups and calculate the SDs. After this, there will be one treatment deltaCt and SD, and one control deltaCt and SD. The control group deltaCt can be subtracted from the treatment group deltaCt to obtain a deltadeltaCt. This is used as above to obtain a fold-change.
The reason that the second situation requires averaging the Ct values and the first situation does not is that the samples in the second situation are not paired with controls. If you have a clinical study in which you have matched your treatment subjects with controls based on age, weight, or other criteria, this is the first situation. This is quite useful with small studies that have differences between their treatment individuals that could make lumping them together suppress the effects of the treatment (which would happen when averaging blood pressures, for example, when some patients are children and some are adults; matching each child to a control child and each adult to a control adult will control for difference within the group so that differences between the groups will be apparent). However, many studies (especially larger ones) randomize their subjects and end up with two groups but no specific pairs. In these cases, you could pair the subjects after the fact, but it would not be as necessary or as beneficial as it is in the first situation. Therefore, averaging the deltaCts before calculating the deltadeltaCt is the better option.
Alternately, 2^-deltaCt can be calculated for each sample. Those can be averaged for each group, and the fold-change would then be calculated by dividing the mean from the treatment group by the mean from the untreated group (see equation below).
Fold change = [2^(-deltaCt(treatment))]/[2^(-deltaCt(control))]
Now, all of this is only valid if the target and reference genes have similar amplification efficiencies, which are influenced by the primers and the PCR conditions. Amplification efficiency is determined by running a PCR plate with different concentrations of the same sample and then plotting the Ct vs. log(concentration). The data points should form a line, with the efficiency equal to 10^(-1/slope). If the efficiencies are not within 10% of each other (and 10% of 2; an amplification efficiency of 2 shows that the DNA doubles each cycle), then the PCR conditions should be altered or new primers should be ordered.
Friday, July 8, 2011
Altered regulation of the rostral ventrolateral medulla in hypertensive obese Zucker rats
Domitila A. Huber and Ann M. Schreihofer
American Journal of Physiology - Heart and Circulatory Physiology 301: H230-H240, 2011
Obese Zucker rats (OZR) are a genetic model of obesity and differ from lean Zucker rats (LZR) in that they lack leptin receptors. Because leptin is a key signaler of satiety, these rats eat much more and become obese. They also have increased sympathetic nerve activity (SNA) and increased mean arterial pressure (MAP). It has been seen that, in human subjects, eliminating SNA causes greater decreases in MAP in obese subjects than in lean subjects, suggesting that the increased SNA contributes to the increased MAP. In rats, this increased sympathetic tone has been traced back to the RVLM. In some models, this is at least partially explained by increases in tonic glutamate and angiotensin II activation and decreased tonic GABA inhibition of the RVLM.
Huber and Schreihofer did recordings from the left greater splanchnic nerve, brain stem microinjections, and histological analysis to further study the source of the differences between the OZRs and LZRs. When muscimol (a GABAA agonist) was injected into the RVLM, the SNA dropped almost to zero and the MAP and heart rate (HR) both decreased significantly, with the MAP decreasing more in the OZR than in the LZR. In the NTS, muscimol injections caused increases in SNA, HR, and MAP, but the OZR had smaller increases than the LZR. Injection of losartan (an AT1 antagonist) into the RVLM produced a decrease in SNA, MAP, and HR in the OZR but only a decrease in HR in the LZR. Kynurenate (an ionotropic glutamate receptor antagonist) injection eliminated the reflex decreases in all three variables in response to stimulation of the sciatic nerve. In the CVLM, kynurenate injections evoked smaller increases in SNA and HR in the OZR than in the LZR. Gabazine (a GABAA antagonist) injected into the RVLM caused a reduction of the reflex decreases in SNA, HR, and MAP in response to stimulation of vagal afferents, but less so in the OZR than in the LZR. Finally, injecting GABA into the RVLM caused similar decreases in SNA, HR, and MAP in both OZR and LZR.
To summarize the results, drug injections into the RVLM showed that the RVLM in the OZR is probably receiving less GABA than in the LZR (because the GABAA agonist caused a greater response and the GABAA antagonist caused a lesser response) and that the NTS is exerting less control in the OZR than in the LZR (because inhibiting is caused a lesser response). The AT1 antagonist caused a greater response in the OZR, indicating that the OZR RVLM is being activated more by angiotensin II than the LZR RVLM is. Knocking out glutamate signalling in the CVLM of the OZR had less of an effect than in the LZR, implying that the CVLM is less active in the OZR (which agrees with the NTS data). In short, the OZR RVLM is receiving less tonic GABA input from the CVLM, receiving more tonic angiotensin II input, and receiving similar tonic glutamate input. The OZR CVLM receives less tonic glutamate (which explains the lower tonic GABA release in the RVLM), and the NTS is also less active (because inhibiting it has a lesser effect).
Huber and Schreihofer suggest several reasons for these differences, but the experiments in this paper assessed only the differences and not their causes. One potential problem with comparing a leptin-receptor deficient rat to a normal rat is that the leptin-receptor deficient rat has higher levels of leptin, which is known to cause increased SNA and MAP. However, it has been shown that the OZR still have higher SNA and MAP and attenuated baroreflexes even when leptin's effects are absent. Also, the MAP and baroreflexes are normal in juvenile OZR, even though they lack the leptin receptor. Experiments in other animal models of hypertension show differences similar to those seen in these rats, as well.
American Journal of Physiology - Heart and Circulatory Physiology 301: H230-H240, 2011
Fat rats!
Huber and Schreihofer did recordings from the left greater splanchnic nerve, brain stem microinjections, and histological analysis to further study the source of the differences between the OZRs and LZRs. When muscimol (a GABAA agonist) was injected into the RVLM, the SNA dropped almost to zero and the MAP and heart rate (HR) both decreased significantly, with the MAP decreasing more in the OZR than in the LZR. In the NTS, muscimol injections caused increases in SNA, HR, and MAP, but the OZR had smaller increases than the LZR. Injection of losartan (an AT1 antagonist) into the RVLM produced a decrease in SNA, MAP, and HR in the OZR but only a decrease in HR in the LZR. Kynurenate (an ionotropic glutamate receptor antagonist) injection eliminated the reflex decreases in all three variables in response to stimulation of the sciatic nerve. In the CVLM, kynurenate injections evoked smaller increases in SNA and HR in the OZR than in the LZR. Gabazine (a GABAA antagonist) injected into the RVLM caused a reduction of the reflex decreases in SNA, HR, and MAP in response to stimulation of vagal afferents, but less so in the OZR than in the LZR. Finally, injecting GABA into the RVLM caused similar decreases in SNA, HR, and MAP in both OZR and LZR.
To summarize the results, drug injections into the RVLM showed that the RVLM in the OZR is probably receiving less GABA than in the LZR (because the GABAA agonist caused a greater response and the GABAA antagonist caused a lesser response) and that the NTS is exerting less control in the OZR than in the LZR (because inhibiting is caused a lesser response). The AT1 antagonist caused a greater response in the OZR, indicating that the OZR RVLM is being activated more by angiotensin II than the LZR RVLM is. Knocking out glutamate signalling in the CVLM of the OZR had less of an effect than in the LZR, implying that the CVLM is less active in the OZR (which agrees with the NTS data). In short, the OZR RVLM is receiving less tonic GABA input from the CVLM, receiving more tonic angiotensin II input, and receiving similar tonic glutamate input. The OZR CVLM receives less tonic glutamate (which explains the lower tonic GABA release in the RVLM), and the NTS is also less active (because inhibiting it has a lesser effect).
Huber and Schreihofer suggest several reasons for these differences, but the experiments in this paper assessed only the differences and not their causes. One potential problem with comparing a leptin-receptor deficient rat to a normal rat is that the leptin-receptor deficient rat has higher levels of leptin, which is known to cause increased SNA and MAP. However, it has been shown that the OZR still have higher SNA and MAP and attenuated baroreflexes even when leptin's effects are absent. Also, the MAP and baroreflexes are normal in juvenile OZR, even though they lack the leptin receptor. Experiments in other animal models of hypertension show differences similar to those seen in these rats, as well.
Wednesday, June 15, 2011
An overview of laser microdissection technologies
Graeme I. Murray
Acta histochemica 109 (2007) 171-176
In this review, Murray compared and contrasted two methods of laser microdissection, giving pros and cons for laser capture microdissection and laser cutting microdissection. Both have the advantage over traditional microdissection techniques (requiring a scalpel or a fine stainless steel needle), because the older methods of microdissection are "slow, cumbersome, and require considerable dexterity." In addition, using a scalpel or needle makes the tissue sample both hard to acquire and easy to contaminate.
Laser capture microdissection (as we do in our lab) requires a membrane attached to a cap, which is placed over the tissue section and melted with a laser. The melted plastic attaches to the sample, cools within milliseconds, and allows the user to pick up only the small spot of the sample that they have chosen. While this is convenient for collecting samples for use with assays that allow for amplification, like DNA and RNA, it is not very useful for protein assays, as the sample will be quite small. The laser and the temperature changes do not seem to harm the sample, and the settings can be adjusted for use with tissues fixed in various ways.
Laser cutting microdissection was developed more recently and involves using a laser to "draw around" the desired cells, allowing them to detach from the slide. The sample is then dropped or catapulted into a tube for collection. This method does not require costly caps, heating and cooling of a membrane, or contact of any kind with the sample (thus cutting down on opportunities for contamination). It allows for greater precision, as well, because laser capture microdissection has a larger laser diameter with a fixed shape (a circle) for sample collection, whereas laser cutting microdissection allows the user to outline their cells exactly (minimizing the collection of non-target cells that are adjacent to the target cell). However, it works best when only a limited number of cells is needed, as laser capture microdissection allows for a more rapid collection of a large number of cells.
As to the matter of tissue preparation, Murray recommends that the tissue be exposed for as little time as possible to stains or rapid IHC techniques. The biggest takeaway for tissue preparation is to avoid doing anything make the sample less ideal for the assays that one intends to do. For us, because we wish to isolate the RNA, that means using fresh frozen tissue and avoiding letting the sections stay at room temperature for very long, so that the RNA has little opportunity to degrade. Different types of molecular analysis will require different treatment of the tissue, though.
Acta histochemica 109 (2007) 171-176
In this review, Murray compared and contrasted two methods of laser microdissection, giving pros and cons for laser capture microdissection and laser cutting microdissection. Both have the advantage over traditional microdissection techniques (requiring a scalpel or a fine stainless steel needle), because the older methods of microdissection are "slow, cumbersome, and require considerable dexterity." In addition, using a scalpel or needle makes the tissue sample both hard to acquire and easy to contaminate.
Laser capture microdissection (as we do in our lab) requires a membrane attached to a cap, which is placed over the tissue section and melted with a laser. The melted plastic attaches to the sample, cools within milliseconds, and allows the user to pick up only the small spot of the sample that they have chosen. While this is convenient for collecting samples for use with assays that allow for amplification, like DNA and RNA, it is not very useful for protein assays, as the sample will be quite small. The laser and the temperature changes do not seem to harm the sample, and the settings can be adjusted for use with tissues fixed in various ways.
Laser cutting microdissection was developed more recently and involves using a laser to "draw around" the desired cells, allowing them to detach from the slide. The sample is then dropped or catapulted into a tube for collection. This method does not require costly caps, heating and cooling of a membrane, or contact of any kind with the sample (thus cutting down on opportunities for contamination). It allows for greater precision, as well, because laser capture microdissection has a larger laser diameter with a fixed shape (a circle) for sample collection, whereas laser cutting microdissection allows the user to outline their cells exactly (minimizing the collection of non-target cells that are adjacent to the target cell). However, it works best when only a limited number of cells is needed, as laser capture microdissection allows for a more rapid collection of a large number of cells.
As to the matter of tissue preparation, Murray recommends that the tissue be exposed for as little time as possible to stains or rapid IHC techniques. The biggest takeaway for tissue preparation is to avoid doing anything make the sample less ideal for the assays that one intends to do. For us, because we wish to isolate the RNA, that means using fresh frozen tissue and avoiding letting the sections stay at room temperature for very long, so that the RNA has little opportunity to degrade. Different types of molecular analysis will require different treatment of the tissue, though.
Monday, June 13, 2011
C1 neurons in the rat rostral ventrolateral medulla differentially express vesicular monoamine transporter 2 in soma and axonal compartments
C. P. Sevigny, J. Bassi, A. G. Teschemacher, K. S. Kim, D. A. Williams, C. R. Anderson and A. M. Allen
European Journal of Neuroscience, Vol. 28 pp. 1536-1544, 2008
C1 neurons of the RVLM are defined as producing adrenaline, but the effect that adrenaline is used to is still not quite worked out; they are generally accepted as using glutamate as their major neurotransmitter. To this end, they express a vesicular glutamate transmitter. They also express a vesicular adrenaline transporter: VMAT2. The authors of this paper looked at the differential expression of VMAT2 in the cell soma and axon terminals to determine if rostrally-located, bulbospinal C1 neurons release adrenaline somatodendritically (which requires cell body expression of VMAT2), synaptically, or both.
A lentivirus was used to identify the C1 neurons in one group of rats, rhodamine fluorescent microspheres were injected into the T3/T4 level of the spinal cord to identify spinally projecting neurons in a second group of rats, and anti-PMNT and anti-VMAT2 antibodies were used to identify PMNT (again, to identify C1 neurons) and VMAT2 in tissue sections. Both brainstem sections and spinal cord sections were examined, to compare cell soma expression of VMAT2 in spinally projecting C1 nuerons to axonal expression in the same neurons. Additionally, spinally projecting C1 neurons were compared to rostrally projecting C1 neurons.
Rostrally projecting C1 neurons did have VMAT2 present in their cell bodies, but bulbospinal C1 neurons, which were identified by the presence of the rhodamine fluorescent microspheres, rarely did. VMAT2 was present in the axon varicosities in the spinal cord, though, suggesting that the bulbospinal C1 neurons do release adrenaline as a neurotransmitter. While bulbospinal C1 neurons may not release adrenaline somatodendritically, though, some C1 neurons are thought to, and bulbospinal neurons seem to be excited to alpha-2-adrenoceptor blockade, suggesting that adrenaline may have a role in regulating the tonic activity of the RVLM. In short, some C1 neurons, but not the bulbospinal C1 neurons, likely release adrenaline within the RVLM (which has an effect on tonic activity in the RVLM) while C1 bulbospinal neurons release adrenaline along with glutamate at their axon terminals.
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