You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


17 Jan 2020

Transgenic mice fail to capture many aspects of Alzheimer’s disease, and a new transcriptional study lays bare one reason. Brain cells in the two species, it turns out, respond quite differently to amyloidosis. In the January 13 Nature Medicine, researchers led by Marco Colonna and Maxim Artyomov at Washington University School of Medicine in St. Louis report distinct transcriptional changes in several cell types in human AD brains compared with those of 5XFAD mice.

Microglia in AD brain turn homeostatic genes up, not down as in mice.

In both species, TREM2 mutation or deletion weakens microglial activation.

Astrocyte and oligodendrocyte responses in AD may be secondary to neuronal death.

In particular, human microglia ramped up a transcriptional program controlled by interferon regulatory factor 8 (IRF8) that had little overlap with the disease-associated microglial (DAM) signature seen in mice. However, in both species, deletion or mutation of TREM2 muted the response to amyloidosis. “This supports the idea that TREM2 mutations impair microglial responses and facilitate the progression of disease,” Colonna told Alzforum. This study is the first to report single-nucleus expression data from people with TREM2 mutations. Some of these data were previously presented at last year’s Brain Conference in Rungstedgaard, Denmark (May 2019 conference news).

“Among the now several ‘omics’ papers on Alzheimer’s disease that have emerged in the last few years, this new report by Zhou et al. stands out because of its comprehensive analysis of mouse and human tissue at the single cell level,” Ashley Bush and Scott Ayton at the University of Melbourne, Australia, wrote to Alzforum (full comment below).

Shane Liddelow at New York University said the field needs to do more such comparative mouse-human studies to parse out what cell types in rodent models best reflect the human disease process. “This paper produces an incredibly rich dataset for the field that will be mined heavily to determine new testable hypotheses … studies like this one pave the way for expanding our combined community dataset so that we can mitigate future mistakes of studying the wrong cellular responses,” he wrote to Alzforum (full comment below).

Species Differences. Gene-expression patterns cluster into distinct cellular groups in 5xFAD mouse (top) and human AD brains (bottom). [Courtesy of Zhou et al., Nature Medicine.]

With the advent of techniques like single-nucleus RNA-Seq, researchers have started to delineate how specific cell types react to disease. Initial single-nucleus transcriptome studies from Li-Huei Tsai and Manolis Kellis at MIT and others reported little evidence for a DAM-like signature in microglia from AD brain, with increased expression of only a few DAM genes, such as ApoE (May 2019 news).

Colonna and colleagues focused on the role of TREM2. For mice, first author Yingyue Zhou ran RNA-Seq on a total of 73,419 nuclei isolated from the cortices of four different strains: wild-type, TREM2 knockout, 5XFAD, and 5XFAD mice lacking TREM2. Zhou and colleagues analyzed nuclei from three mice from each genotype, all of them 7 months old. By similarity, the gene-expression patterns clustered into 10 groups, representing five neuronal and five non-neuronal cell types. The non-neuronal comprised microglia, astrocytes, oligodendrocytes, oligodendrocyte precursor cells (OPCs), and endothelial cells (see image above).

To compare with human brain, the authors isolated cells from dorsolateral prefrontal cortex samples taken postmortem from 11 control and 11 AD brains, as well as 10 brains of AD patients with a R62H TREM2 mutation. Samples came from the Rush Memory and Aging cohort. Altogether, the authors analyzed 66,311 nuclei. Gene-expression profiles clustered differently than in mice, falling into three neuronal and two oligodendrocyte subtypes, and one each for microglia, astrocytes, OPCs, and endothelial cells (see image above).

The greatest species differences in the amyloid response were in microglia. In mice with amyloidosis, these immune cells dialed down homeostatic genes and turned up DAM genes, in agreement with previous studies (May 2017 news; Jul 2018 conference news). Lack of TREM2 muted the DAM response, and reduced the number of microglia seen in 5XFAD cortex.

In human AD brain, by contrast, microglia turned homeostatic genes up, not down. This seemed to be controlled by increased expression of the transcription factor IRF8, which is known to activate homeostatic genes. Human microglia bumped up expression of AD risk genes that were not changed in the DAM profile: SORL1, alpha-2 macroglobulin, and chitinase-3-like protein 1, also known as YKL-40. The only overlap with the DAM transcriptome was an increase in TREM2, ApoE, CD68, and MHC-II genes. Overall, the human microglial profile resembled an IRF8-driven response seen in spinal cord microglia after peripheral nerve injury (Masuda et al., 2012). The R62H TREM2 variant, an AD risk factor, softened this response. “The effect of the mutation was not dramatic, but likely snowballs over the lifespan of an individual,” Colonna said.

Oleg Butovsky at Brigham and Women’s Hospital, Boston, called the differences between mouse and human homeostatic responses fascinating, noting that they might represent a biological dichotomy between species. “This needs to be explored and understood mechanistically. That will give us additional directions in terms of treatment,” he said.

Parsing the data by biological processes, microglia in AD brain most altered expression of genes involved in iron metabolism. They suppressed transcription of genes involved in iron transport and storage, such as SLC25A37, HAMP, and FTH1. Iron buildup has been linked to cognitive decline in AD (Mar 2019 news).

After microglia, oligodendrocytes changed most in AD and in mouse models, but again profiles were species-specific. In 5XFAD mice, these cells boosted expression of the complement factor C4b and the serine peptidase inhibitor Serpina3n, as well as the immune histocompatibility gene H2-d1. The increases were smaller in 5XFAD mice lacking TREM2, suggesting these changes partially depended on TREM2 signaling. The effects of these changes were unclear, although the authors found that C4b and Serpina3n independently promoted Aβ aggregation in vitro. In 5XFAD brain, Serpina3n+ oligodendrocytes tended to cluster near plaques. In addition, more oligodendrocytes populated 5XFAD brains than controls, suggesting enhanced proliferation of these cells.

In AD brain, on the other hand, oligodendrocytes took on a different profile, turning down genes involved in myelination, axon guidance, and differentiation, while ramping up genes for oxidative stress and lipid accumulation. The changes may be a response to axon loss and the subsequent buildup of debris in AD brain, the authors suggested. These oligodendrocyte changes were muted in R62H carriers. Curiously, C4b and Serpina3n were not expressed in human oligodendrocytes, but in astrocytes instead.

In mouse brain, astrocytes and other cell types were remarkably similar in wild-type and 5XFAD animals. In human brain, however, astrocytes turned up C4b expression and dampened Serpina3 in AD. It is unclear why Serpina3 changed in the opposite direction and in a different glial cell type than in mice. Overall, AD astrocytes dialed down expression of genes responsible for coordinating lipid metabolism with neurons, and bumped up expression of genes for extracellular matrix. Normally, astrocytes provide metabolic support to neurons, aiding in fatty-acid storage and detoxifying reactive oxygen species.

The findings dovetail with a previous study that reported astrocytes in AD brain poorly handle lipids and overexpress matrix proteins (Aug 2019 news). The human astrocyte-expression profile was distinct from a previously described inflammatory astrocyte signature, the authors noted (Jan 2017 news). The R62H variant had little effect on astrocytes.

RNA-Seq only captures a small portion of the total cells in a tissue, and requires nucleotide amplification, which can introduce errors. Therefore the authors carried out a quantitative analysis of gene expression in bulk AD brain tissue, as well. They found a dearth of transcripts for genes involved in synaptic transmission, axon guidance, and cell survival. These changes probably reflect neuron loss, Colonna said. He believes the alterations in astrocytes and oligodendrocytes may be secondary to neuronal loss as well.

“I think the major difference between human AD and mouse models is that there is much more neuronal cell death in human disease,” Colonna said. Liddelow thinks this has broad implications for understanding disease. “Changes in one cell type may not necessarily mean these cells are … driving disease pathology, but instead may simply be responding to the changing metabolic needs of the cells around them,” he wrote.

To look more specifically at the effects of TREM2 mutations, the authors analyzed bulk gene expression of parietal cortex samples from five carriers of the R47H TREM2 mutation who had AD, and compared the findings with five control AD brains. These samples came from the Alzheimer’s Disease Research Center at WashU. R47H carriers had a less-pronounced microglial response to disease, with lower levels of expression of IRF8 and downstream genes. The effect of R47H was stronger than that of R62H, the authors noted, in agreement with the greater AD risk conferred by the former.

Intriguingly, some expression differences between TREM2 genotypes did emerge, noted Zhuoran Yin in Butovsky’s group. In the Rush R62H AD samples, some homeostatic microglial genes, such as TMEM119 and TGFB1, were suppressed compared to AD brain without a TREM2 mutation, she pointed out. In the WashU R47H cohort, this did not happen. It is unclear if the difference is due to TREM2 genotype, the brain region examined, the demographics of the cohort, or some other reason.

Overall, data from this study agrees with previous single-nucleus RNA studies of human brain. There are some differences, though. For example, a recent study of expression in AD entorhinal cortex reported a downregulation of microglial homeostatic genes, and an upregulation of myelination genes in oligodendrocytes, opposite to the effects seen by Colonna and colleagues (Nov 2019 news).

Colonna is now examining larger, more diverse human brain samples, hoping to parse out differences due to demographics and brain region. He will also try to correlate gene-expression changes with disease progression, to see if expression changes substantially from one stage to the next.

Lastly, the work might uncover new biomarkers. Several of the genes identified as altered in AD brain, such as NfL, YKL-40, SORL1, BDNF, and ADCYAP1, encode known fluid biomarkers for the disease. “By examining this transcriptome database, we may identify additional biomarkers for disease progression,” Colonna suggested.—Madolyn Bowman Rogers