Well done! Finally, some systematic transcriptome profiling of the human brain on a large scale. If we are ever going to crack neurodevelopmental disorders, such datasets will be absolutely critical. Exon-level transcriptome and associated genotyping data, brain regions, gender differences, developmental trajectories—this manuscript has it all. However, this is only a start, a catalogue of molecular events that begs to be explored. We see the complexity contained within the dataset, and it is simply mind-boggling. How do we make sense out of all this? Which changes are characteristic of interneurons, and which trajectories are projection neuron derived? How are the changes related to maturation of layers or various diseases? The mining of this dataset is far from over. It will be interesting to see what a WGCNA type of analysis will uncover in this proverbial gold mine. We need new ideas, we need new bioinformatic tools to look at this.

In addition, based on the presented data, we need to form precise, testable hypotheses. And then will come the hardest part—we...  Read more

Well done! Finally, some systematic transcriptome profiling of the human brain on a large scale. If we are ever going to crack neurodevelopmental disorders, such datasets will be absolutely critical. Exon-level transcriptome and associated genotyping data, brain regions, gender differences, developmental trajectories—this manuscript has it all. However, this is only a start, a catalogue of molecular events that begs to be explored. We see the complexity contained within the dataset, and it is simply mind-boggling. How do we make sense out of all this? Which changes are characteristic of interneurons, and which trajectories are projection neuron derived? How are the changes related to maturation of layers or various diseases? The mining of this dataset is far from over. It will be interesting to see what a WGCNA type of analysis will uncover in this proverbial gold mine. We need new ideas, we need new bioinformatic tools to look at this.

In addition, based on the presented data, we need to form precise, testable hypotheses. And then will come the hardest part—we need to test these hypotheses, and this will be incredibly time consuming and very low throughput. From in-vitro systems, transgenic models, electrophysiology, neurochemistry to imaging, we should use everything at our disposal.

While the generation of this dataset is clearly long overdue, I also must note the enormous price tag that these experiments carry. Very few laboratories/groups in the world have resources to perform such studies, and such fishing expeditions/dataset-generation projects are poorly suited to regular NIH-funded mechanisms.

The Nature papers by Colantuoni et al. (2011) and Kang et al. (2011) are landmark studies, not only because of the wealth of data about the human brain transcriptome across the lifespan that they contain, but as a resource for other researchers to dip into or mine as they wish. Both papers represent the culmination of extensive research programs, and are based ultimately on the crucial, sensitive, and often unappreciated task of collecting a sufficient number of well-characterized brains (Deep-Soboslay et al., 2011). In turn (as noted by Karoly Mirnics in his comment), they also attest to the importance of having funding schemes which permit this kind of ambitious, long-term, large-scale—and expensive—research. The papers set a new gold standard for human brain studies in terms of size and scope. They also illustrate the renaissance of postmortem brain research, and provide confirmation (if any was needed) that human brain diseases need direct study of human brains—including normative analyses across the...  Read more

The Nature papers by Colantuoni et al. (2011) and Kang et al. (2011) are landmark studies, not only because of the wealth of data about the human brain transcriptome across the lifespan that they contain, but as a resource for other researchers to dip into or mine as they wish. Both papers represent the culmination of extensive research programs, and are based ultimately on the crucial, sensitive, and often unappreciated task of collecting a sufficient number of well-characterized brains (Deep-Soboslay et al., 2011). In turn (as noted by Karoly Mirnics in his comment), they also attest to the importance of having funding schemes which permit this kind of ambitious, long-term, large-scale—and expensive—research. The papers set a new gold standard for human brain studies in terms of size and scope. They also illustrate the renaissance of postmortem brain research, and provide confirmation (if any was needed) that human brain diseases need direct study of human brains—including normative analyses across the lifespan—if their genetic, neurodevelopmental, and molecular aspects are to be understood (Kleinman et al., 2011).

The papers will take time to digest fully. Early impressions reveal several findings of particular interest and relevance to schizophrenia.

1. It's striking just how dramatic are the transcriptional changes, even across a restricted fetal time period. Simple notions of a "second trimester" origin of a disorder need to become more nuanced.

2. The flow of alterations between fetal and infant life, and the infant-aging similarities and differences also speak to the dynamic temporal nature of the transcriptome, its regulation, refinement, and recapitulation.

3. The extent of regional (and sex) differences in gene expression and exon usage—and the interactions of these with development—found by Kang et al. are noteworthy, too, again attesting to the sheer complexity of the transcriptomic landscape.

4. The eQTL data in both studies emphasize the importance of cis variation in regulation of gene expression, especially for SNPs around transcriptional start sites; the P value of 10-78 (Fig. 3b in Colantuoni et al.) must be a record for a human brain study!

The data provide a much more detailed (albeit more complex) context within which to interpret deviations from the normal transcriptional profile in those with, or at risk of, schizophrenia. Notwithstanding the huge number of data in these papers, many questions remain unanswered. There is a relative gap across mid-childhood—for obvious reasons—which later studies can fill in (c.f. the accompanying Nature editorial on the need to collect more brains from children). Future studies will also hopefully move to sequencing methods, extend to other brain regions, and address the daunting task of protein-based equivalent studies. Finally, as the authors of both papers note, the current data are from tissue homogenates, and so cannot reveal differential changes in one cell type from another. We can expect these last differences to be as complicated and fascinating as the temporal and regional profiles reported here.

A key issue for researchers interested in the neurobiology of genes involved in schizophrenia is how deep to dig when investigating the expression of a gene (as one aspect of its function or pathology) before deciding enough is enough. The data in these papers indicate that the answer is probably "very deep." Stretching the metaphor, the data also highlight that there may need to be several digs, across time and space, in looking for different kinds of molecular treasure.

References:

Deep-Soboslay A, Benes FM, Haroutunian V, Ellis JK, Kleinman JE, Hyde TM. Psychiatric brain banking: three perspectives on current trends and future directions. Biol Psychiatry . 2011 Jan 15 ; 69(2):104-12. Abstract

Kleinman JE, Law AJ, Lipska BK, Hyde TM, Ellis JK, Harrison PJ, Weinberger DR. Genetic neuropathology of schizophrenia: new approaches to an old question and new uses for postmortem human brains. Biol Psychiatry . 2011 Jan 15 ; 69(2):140-5. Abstract

Just a passing comment. I believe the study by Kang et al. shows an interesting change in gene expression of the MIR137, which was strongly implicated by GWAS.

Both of these papers are extremely useful, and welcomed for the study of eQTLs in human brain.

View all comments by Marquis Vawter

These two new papers show the spatial and temporal regulation of gene expression in the human brain across various ages. Although it is not novel to observe various patterns of gene expression during human brain development, systematic bioinformatics approaches using such enormous sample sizes will lead us to a new level of understanding the complexity of the transcriptome during development.

Both groups showed that age is a very strong contributor to global differences in gene expression compared to other variables such as sex, ethnicity, and inter-individual variation. Thus, transcriptional differences and changes are most pronounced during early development, gradually slowing through infancy, adolescence, and into adulthood—each stage having a clear transcriptional profile. Kang et al. further showed that gene expression is also spatially regulated. Furthermore, they found many co-expressed gene groups that were spatially and temporally regulated. They also reported sex-biased gene expression.

Our group, like many other laboratories, is trying to approach...  Read more

These two new papers show the spatial and temporal regulation of gene expression in the human brain across various ages. Although it is not novel to observe various patterns of gene expression during human brain development, systematic bioinformatics approaches using such enormous sample sizes will lead us to a new level of understanding the complexity of the transcriptome during development.

Both groups showed that age is a very strong contributor to global differences in gene expression compared to other variables such as sex, ethnicity, and inter-individual variation. Thus, transcriptional differences and changes are most pronounced during early development, gradually slowing through infancy, adolescence, and into adulthood—each stage having a clear transcriptional profile. Kang et al. further showed that gene expression is also spatially regulated. Furthermore, they found many co-expressed gene groups that were spatially and temporally regulated. They also reported sex-biased gene expression.

Our group, like many other laboratories, is trying to approach molecular mechanism(s) underlying schizophrenia by using patient-derived cells, especially induced pluripotent stem cells (Dolmetsch and Geschwind, 2011) and immature neurons obtained from nasal biopsy (Sawa and Cascella, 2009). The challenge in this approach has been the shortage of information on gene expression patterns during the neurodevelopmental trajectory. In this sense, these two outstanding papers provide all of us with useful information. If any future studies can address the spatial and temporal regulation of gene expression in each “specific” type of brain cell, this will be of further help to the field. Laser-captured microdissection could be a useful tool to obtain enriched populations of different cell types from tissue (Goswami et al., 2010; Tajinda et al., 2010). Such encyclopedia-type efforts may also be applied to reveal the epigenetic landscape of the brain in the future (Cheung et al., 2010).

References:

Dolmetsch R, Geschwind DH. The human brain in a dish: the promise of iPSC-derived neurons. Cell . 2011 Jun 10 ; 145(6):831-4. Abstract

Sawa A, Cascella NG. Peripheral olfactory system for clinical and basic psychiatry: a promising entry point to the mystery of brain mechanism and biomarker identification in schizophrenia. Am J Psychiatry . 2009 Feb 1 ; 166(2):137-9. Abstract

Goswami DB, May WL, Stockmeier CA, Austin MC. Transcriptional expression of serotonergic regulators in laser-captured microdissected dorsal raphe neurons of subjects with major depressive disorder: sex-specific differences. J Neurochem . 2010 Jan 1 ; 112(2):397-409. Abstract

Tajinda K, Ishizuka K, Colantuoni C, Morita M, Winicki J, Le C, Lin S, Schretlen D, Sawa A, Cascella NG. Neuronal biomarkers from patients with mental illnesses: a novel method through nasal biopsy combined with laser-captured microdissection. Mol Psychiatry . 2010 Mar 1 ; 15(3):231-2. Abstract

Cheung I, Shulha HP, Jiang Y, Matevossian A, Wang J, Weng Z, Akbarian S. Developmental regulation and individual differences of neuronal H3K4me3 epigenomes in the prefrontal cortex. Proc Natl Acad Sci U S A . 2010 May 11 ; 107(19):8824-9. Abstract

This is perhaps the best and most comprehensive transcriptome dataset of schizophrenia generated to date. It has multiple strengths, including the use of four different cortical regions, the correlation of genetics with transcriptomics, and the use of strong bioinformatics including WGCNA analysis. The findings are quite revealing. The results suggest that there is a strong, common signature across brain areas BA21, BA32, and BA38 that encompasses genes related to transcription/translation, signal transduction, the cell cycle, cell adhesion, the immune response, apoptosis, and the cytoskeleton.

Perhaps surprisingly, the expression signature was far less prominent in prefrontal cortical area BA 46, which is one of the most affected regions in schizophrenia. However, it was also clear that each brain region had a unique, region-specific schizophrenia signature. In addition, this study reproduced and validated a number of previously reported findings related to oligodendrocyte and mitochondrial transcript deficits. Nevertheless, the results of the current study disagree with...  Read more

This is perhaps the best and most comprehensive transcriptome dataset of schizophrenia generated to date. It has multiple strengths, including the use of four different cortical regions, the correlation of genetics with transcriptomics, and the use of strong bioinformatics including WGCNA analysis. The findings are quite revealing. The results suggest that there is a strong, common signature across brain areas BA21, BA32, and BA38 that encompasses genes related to transcription/translation, signal transduction, the cell cycle, cell adhesion, the immune response, apoptosis, and the cytoskeleton.

Perhaps surprisingly, the expression signature was far less prominent in prefrontal cortical area BA 46, which is one of the most affected regions in schizophrenia. However, it was also clear that each brain region had a unique, region-specific schizophrenia signature. In addition, this study reproduced and validated a number of previously reported findings related to oligodendrocyte and mitochondrial transcript deficits. Nevertheless, the results of the current study disagree with the previously reported and replicated outcomes of similar assessments of other cohorts: in this study, GABA system genes were upregulated, and gene ontology categories related to immune response were downregulated in subjects with schizophrenia.

This is certainly noteworthy, and this apparent discrepancy in findings will have to be addressed by future experiments. I wish that the authors had addressed this issue in their discussion. The combined transcriptomics-genetics results suggest that the oligodendrocyte, GABA, and glutamate modules are (at least partially) driven by genetic vulnerability, while other gene expression changes might be secondary/adaptational in nature.

Finally, the study suggests that interregional coexpression is attenuated in schizophrenia. A very similar hypothesis, using WGCNA analysis of samples with autism, has been proposed in autism by the Geschwind laboratory (see Voineagu et al., 2011, and the commentary of Korade and Mirnics, 2011). Voineagu et al. reported that the differential patterns of gene expression that normally distinguish the frontal and temporal cortices are significantly attenuated in the autistic brain, potentially leading to loss of functional specifications across the affected cortical areas. This is certainly worth further exploration, and the schematic hypothesis presented in Figure 5 of the current paper is a logical blueprint that nicely maps out a possible sequence of pathophysiological events in schizophrenia. Still, the actual sequence of the proposed events can be debated at the current time: it is very likely that the cascades proposed by Figure 1 in the commentary by Korade et al. and Figure 5 in the current manuscript will have to be revised as our knowledge accumulates.

References:

Voineagu I, Wang X, Johnston P, Lowe JK, Tian Y, Horvath S, Mill J, Cantor RM, Blencowe BJ, Geschwind DH. Transcriptomic analysis of autistic brain reveals convergent molecular pathology. Nature . 2011 May 25. Abstract

Korade Z, Mirnics K. Gene expression: the autism disconnect. Nature . 2011 June 15. Abstract